Mortality predictors of hospitalized patients with COVID-19: Retrospective cohort study from Nur-Sultan, Kazakhstan.

BACKGROUND: First reported case of Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) in Kazakhstan was identified in March 2020. Many specialized tertiary hospitals in Kazakhstan including National Research Cardiac Surgery Center (NRCSC) were re-organized to accept coronavirus disease 2019 (COVID-19) infected patients during summer months of 2020. Although many studies from worldwide reported their experience in treating patients with COVID-19, there are limited data available from the Central Asia countries. The aim of this study is to identify predictors of mortality associated with COVID-19 in NRCSC tertiary hospital in Nur-Sultan, Kazakhstan.
METHODS: This is a retrospective cohort study of patients admitted to the NRCSC between June 1st-August 31st 2020 with COVID-19. Demographic, clinical and laboratory data were collected from electronic records. In-hospital mortality was assessed as an outcome. Patients were followed-up until in-hospital death or discharge from the hospital. Descriptive statistics and factors associated with mortality were assessed using univariate and multivariate logistic regression models.
RESULTS: Two hundred thirty-nine admissions were recorded during the follow-up period. Mean age was 57 years and 61% were males. Median duration of stay at the hospital was 8 days and 34 (14%) patients died during the hospitalization. Non-survivors were more likely to be admitted later from the disease onset, with higher fever, lower oxygen saturation and increased respiratory rate compared to survivors. Leukocytosis, lymphopenia, anemia, elevated liver and kidney function tests, hypoproteinemia, elevated inflammatory markers (C-reactive protein (CRP), ferritin, and lactate dehydrogenase (LDH)) and coagulation tests (fibrinogen, D-dimer, international normalized ratio (INR), and activated partial thromboplastin time (aPTT)) at admission were associated with mortality. Age (OR 1.2, CI:1.01-1.43), respiratory rate (OR 1.38, CI: 1.07-1.77), and CRP (OR 1.39, CI: 1.04-1.87) were determined to be independent predictors of mortality.
CONCLUSION: This study describes 14% mortality rate from COVID-19 in the tertiary hospital. Many abnormal clinical and laboratory variables at admission were associated with poor outcome. Age, respiratory rate and CRP were found to be independent predictors of mortality. Our finding would help healthcare providers to predict the risk factors associated with high risk of mortality. Further investigations involving large cohorts should be provided to support our findings.

Introduction

The first case of pneumonia of unknown etiology was reported in Wuhan, China in December 2019 [1]. On 11th of February, 2020 World Health Organization (WHO) named the disease coronavirus disease 2019 (COVID-19) and it was identified to be caused by the virus from coronavirus family—SARS-CoV-2 [1]. Rapid spread of the disease over first months of 2020 led to the escalation of the situation and in March 2020 WHO has declared a worldwide pandemic caused by SARS-CoV-2 [1].

The first registered case of COVID-19 in Kazakhstan was identified on March 13th 2020 followed by an introduction of a country-wide state of emergency on March 16th 2020 [2,3]. During that time Kazakhstani healthcare facilities have suddenly been overwhelmed with COVID-19 cases, which resulted in critical filling of infectious hospitals with patients all over Kazakhstan [3]. This is why during the peak of the pandemic many specialized tertiary hospitals were immediately transferred to the infectious diseases profile. Often the most severe cases and critically ill patients were admitted to these hospitals, which have sufficient expertise in advanced intensive care unit (ICU care). One of such hospitals was the National Research Cardiac Surgery Center (NRCSC)–a tertiary-level, highly specialized hospital in Nur-Sultan, Kazakhstan. From June to August 2020 the center has been closed for new cardiac admissions and focused solely on treatment of COVID-19 patients.

From the very beginning of the pandemic scientific and medical communities were trying to identify demographic, clinical and laboratory peculiarities that could help to predict the course and outcome and guide treatment. It has been proposed that older age, male gender and number of comorbidities could increase the risk for severe course and poor outcome of the disease [4,5].

Many clinical and laboratory markers have been studied to date [5]. Preliminary studies showed that the level of pro-inflammatory cytokines could correlate with the most severe forms of COVID-19 leading to acute respiratory distress syndrome (ARDS) [5]. Current clinical guides suggest using D-dimers and lactate dehydrogenase (LDH) as possible markers to identify patients at risk of deadly complications [5,6]. In addition, some commonly assessed hematological parameters including white blood cell count (WBC), lymphocyte count and C-reactive protein (CRP) were proposed as markers for disease severity and poor outcome [5]. Moreover, clinical findings could also correlate with the course of the disease. Period from disease onset to hospitalization, degree of lung involvement based on computerized tomography (CT) could also play an important role in determining the risk for severe disease course [5].

In the face of the COVID-19 pandemic, a number of tertiary hospitals around the globe had to re-organize into the COVID-centers to help with the overflow in the infectious disease centers [6-8]. Similarly, in Kazakhstan, during the peak of pandemic various hospitals were re-organized into COVID centers. One of them was the National Research Cardiac Surgery Center. Often the most severe cases and critically ill patients were referred to our hospital, which has sufficient experience and equipment to provide advanced floor and ICU care. This could explain why the case fatality rate in our study 3–4 folds was higher than nationwide or worldwide COVID-19 fatality rate of 3–4% [2,9].

Indeed, demographical, clinical and laboratory data obtained from patients at admission are of particular interest as independent predictors of poor outcome, since they can be easily obtained and used to triage patients for further distribution into highly specialized facilities for appropriate care. However, the greatest part of knowledge on this topic comes from the developed world, while data from developing countries is still lacking. It is important to compare the local results with worldwide experience. Therefore, the aim of this retrospective cohort study is to identify demographic, clinical and laboratory predictors of mortality of patients with COVID-19 admitted to the tertiary hospital during June-August of 2020 using detailed patient-level clinical information.

Methods

Study design and participants

This is a retrospective cohort study, conducted at the National Research Cardiac Surgery Center (NRCSC)–a tertiary highly specialized hospital in Nur-Sultan, Kazakhstan. All 239 patients admitted to the hospital with COVID-19 disease during the period of June 16th–October 16th 2020 were included in the study. No patients were excluded from the study. Patients were admitted through the emergency department. Diagnosis of COVID-19 was defined as either a positive nasopharyngeal swab for SARS-CoV-2 by reverse transcriptase polymerase chain reaction (PCR) and/or clinically supported evidence of SARS-CoV-2 in the absence of polymerase chain reaction (PCR) confirmed test. Nasopharyngeal swabs were collected at the time of admission and tested with RT-PCR assay. Clinical diagnosis was based on signs and symptoms along with radiological findings suggestive of COVID-19 pneumonia. Laboratory and radiological tests were performed in accordance with the national clinical guidelines for treatment of COVID-19 patients. Patients were managed with supportive care and specific pharmacological therapies in accordance with national and hospital protocols for management of COVID-19 guided by the Ministry of Health of the Republic of Kazakhstan.

Data collection

Demographic data and details of medical history at baseline were collected from hospital electronic records. These characteristics included age, gender, body mass index (BMI), day of disease from initial symptoms to admission, oxygen saturation, cough, temperature, respiratory rate, disease severity, history of hypertension, diabetes, cardiovascular disease, previous cardiac surgery and results of lung computed tomography.

All laboratory data were collected and measured at the baseline during the first day of admission and included but not limited to complete blood counts (CBC) glycated hemoglobin (HbA1c), serum urea, creatinine, alanine transaminase (ALT), aspartate transaminase (AST), total bilirubin, total protein, albumin, lactate dehydrogenase (LDH), C-reactive protein (CRP), D-dimer, fibrinogen, International normalized ratio (INR), Activated partial thromboplastin time (aPTT), ferritin. Data on in-hospital complications like acute kidney injury (AKI) as well as provided treatment such as supplemental oxygen, mechanical ventilation, renal replacement therapy, extracorporeal membrane oxygenation (ECMO), antiviral (Favirapin, Lopinovir/Ritonavir, Remdesivir), antibiotics (Cefuroxim, Cefazolin, Ceftriaxon, Cefepim, Moxifloxacin, Aztreonam, Meropinem, Levafloxacin), antimicrobial drug (Fluconazole) anticoagulation and steroids were recorded.

Ethical statement

The study was approved by the Institutional Review Ethics Committee of the National Research Cardiac Surgery Center (#01-97/2021 from 22/04/21) with exemption from informed consent. There were no known risks to participants expected. During the data collection, all personal information of patients is encoded and the data is depersonalized, so we protect the rights of patients and do not disclose their personal information. Researchers received the electronic database only with information about demographic and clinical characteristics of patients, which was analyzed and reported only in aggregated form, further assuring confidentiality of data.

Study definitions

Severity of the disease was evaluated based on clinical symptoms in accordance with national clinical guidelines (7). The disease severity was stratified as: mild disease—body temperature <38°C, heart rate <90 bpm, respiratory rate <24/min; moderate disease—body temperature 38.1–39°C, heart rate 90–120 bpm, respiratory rate > 24/min; and severe disease—high temperature (over 39°C) with severe symptoms (headache, myalgia, nausea), heart rate > 120 bpm, respiratory rate >28/min. Acute kidney injury was ascertained using the Kidney Disease Improving Global Outcomes (KDIGO) criteria (8). The respiratory failure was stratified as: stage 1—dyspnea, restlessness, RR 25–30 breaths per minute (bpm), PaO2 70 mmHg, PaCO2 50 mmHg; stage 2—cyanosis, RR 35–40 bpm, tachycardia, elevated BP, PaO2 60 mmHg, PaCO2 60 mmHg; and stage 3—hypoxic coma, dilated pupils, shock, arrhythmia. Stages of lung involvement based on CT scan also classified as: CT stage 1 <25%, CT stage 2—25–50%, CT stage 3 50–75%, and CT stage 4 > 75% as defined by the national guideline for treatment of COVID-19 patients in adults (7).

Outcome variable

All of the patients were followed-up from admission to either until discharge from the hospital or in-hospital death. The outcome of interest was in-hospital mortality and associate factors.

Statistical analysis

Descriptive data are presented as percentages for categorical variables and as mean ± standard deviation (SD) or median (inter quartile range—IQR), as appropriate. All cohorts were divided into two groups as survivors and deceased patients. Categorical variables were compared using χ2 tests. Continuous variables were compared using t tests or Mann–Whitney U tests. The association between demographics, clinical, laboratory, treatment related variable and risk of in-hospital death were examined using unadjusted (univariate) and adjusted (multivariate) logistic regression analyses. The following potential confounders were included in the multivariable adjusted model based on theoretical considerations based on previous results from the literature as well as the statistical significance level of univariate analysis: Model 1: age, sex, disease duration at admission, respiratory rate at admission; Model 2: in addition to variables from model 1 included laboratory data as WBC, serum creatinine, total protein, fibrinogen, hemoglobin; Model 3: in addition to variables from model 2 included antivirals and corticosteroid medications. Because of many missing data, those variables which are less than 200 observations (>15% missing data) were not included to the regression analysis.

P values are two-sided and reported as significant at <0.05 for all analyses. All analyses were conducted using STATA IC Version 15.1 (STATA Corporation, College Station, TX).

Results

Baseline characteristics

There were 205 patients (85.8%) who survived the disease and 34 patients (14.2%) died during hospitalization. All demographic and baseline clinical and laboratory characteristics of the cohort are summarized in Table 1. One hundred fifty—one patients (63.2%) had PCR confirmed COVID-19 diagnosis. Mean age was 57.3±12.7 years, 61.1% were males (Fig 1). Median length from disease onset to admission was 10 days (IQR 7–14) and non-survivors were admitted to the hospital later than survivors from the beginning of the disease onset. Moreover, at the time of admission non-survivors had lower oxygen saturation, elevated temperature and respiratory rate, (Figs 2–4) higher grade of disease severity, higher stage of respiratory failure and greater degree of lung damage on CT compared to survivors.

Table 1

General characteristics of admitted COVID-19 patients.

Characteristic	Total (n = 239)	Survived (n = 205)	Deceased (n = 34)	p value
Age, year (mean ± SD)	57.3±12.7	56.6±12.96	61.5±10.5	0.037
Gender, n (%)				0.047
Male,	146 (61.1)	120 (58.6)	26 (76.5)
Female,	93 (38.9)	85 (41.5)	8 (23.5)_
BMI (mean ± SD)	28.9±5.01	28.7±5.2	30.2±3.3	0.15
Obesity stages, n (%)				0.085
Under-weight	2 (0.4)	2 (1.1)	0 (0)
Normal-weight	38 (17.2)	38 (20.3)	0 (0)
Over-weight	92 (43.0)	79 (42.3)	13 (52.00)
Obese	80 (37.4)	68 (36.4)	12 (48)
Clinical findings at admission
Day of disease, days (median [IQR])	10 [7–14]	10 [7–14]	14 [9.5–18.5]	0.0048
Saturation, %/100 (mean ± SD)	89±10	92±6	76±15	<0.0001
Cough, n (%)	130 (54.4)	114 (55.6)	16 (47.1)	0.35
Temperature, °C (mean ± SD)	37.97±0.78	37.92±0.77	38.32±0.76	0.022
Respiratory rate, breath/min (mean ± SD)	21.6±4.1	21.28±3.91	26.3±3.9	<0.0001
Severity stage, n (%):				<0.0001
Mild	16 (6.7)	16 (7.8)	0 (0)
Moderate	178 (74.5)	178 (86.8)	0 (0)
Severe	45 (18.8)	11 (5.4)	34 (100)
Respiratory failure stage, n (%)				<0.0001
0	43 (17.9)	41 (20)	2 (5.9)
1	132 (55.2)	132 (64.4)	0
2	22 (9.2)	22 (10.7)	0
3	42 (17.6)	10 (4.9)	32 (94.1)
Comorbidities
Diabetes, n (%)	53 (22.2)	43 (20.98)	10 (29.4)	0.27
HbA1c>6.5%, n (%)	47 (50)	37 (45.7)	10 (76.9)	0.036
HTN, n (%)	126 (52.7)	112 (54.6)	14 (41.2)	0.14
Cardiovascular disease, n (%)	69 (28.3)	61 (29.8)	8 (23.5)	0.46
Previous cardiac surgery, n (%)	55 (23.0)	44 (21.5)	11 (32.4)	0.16
Radiological lung findings
Maximum % lung damage (CT), % (Median [IQR])	40 [20–60]	30 [20–50]	60 [40–80]	0.0004
CT stage, n (%)				0.008
1-stage (<25% damage)	55 (28.6)	55 (31.1)	0 (0)
2-stage (25–49% damage)	80 (41.7)	74 (41.8)	6 (40)
3-stage (50–74% damage)	40 (20.8)	35 (19.8)	6 (33.3)
4-stage (≥75% damage)	17 (8.9	13 (7.3)	4 (26.7
Polisegmental features, n (%)	191 (79.9)	158 (77.1)	33 (97.1)	0.007
Bilateral opacities, n (%)	199 (93.4)	166 (92.2)	33 (100)	0.097
Pulmonary fibrosis, n (%)	16 (6.7)	14 (6.8)	2 (5.9)	0.84
Laboratory findings at admission
Urea mg/dl (median [IQR])	33.3 [25.1–43.5]	31.6 [24.7–40.6]	55.7 [37.8–99.6]	<0.0001
Creatinine mg/dl (median [IQR])	0.9 [0.7–1.0]	0.9 [0.7–1.0]	1.0 [0.8–1.7]	0.0025
ALT U/l (median [IQR])	30.6 [18.7–50.1]	29.1 [18.5–50.3]	35.9 [22.2–50.1]	0.18
AST U/l (median [IQR])	33.1 [22.1–49.7]	31.9 [21.9–48]	45.4 [30.7–66]	0.0034
Total bilirubin mg/dl (median [IQR])	0.6 [0.4–0.8]	0.5 [0.40–0.8]	0.8 [0.6–1.2]	<0.0001
Total protein g/dl (mean ± SD)	6.6±0.7	6.7±0.6	5.9±0.9	<0.0001
Albumin g/dl (mean ± SD)	3.5±0.7	3.7±0.6	2.8±0.6	<0.0001
LDH, U/L (median [IQR])	255 [190–342]	247 [190–330.6]	370 [318–556]	0.0064
CRP mg/dl (median [IQR])	3.5 [0.9–9.1]	2.9 [0.6–7.8]	12.8 [7.4–23.8]	<0.0001
HbA1c % (mean ± SD)	7.1±1.8	6.99±1.8	7.4±1.2	0.42
D-dimer, ng/ml (median [IQR])	0.6 [0.3–1.2]	0.6 [0.3–0.9]	2.9 [1.0–7.7]	<0.0001
Fibrinogen g/l (median [IQR])	4.2 [3.2–5.3]	4.1 [3.3–5.0]	5.1 [3.2–6.6]	0.043
INR (median [IQR])	0.96 [0.9–1.1]	0.9 (0.9–1.0]	1.1 {1.0–1.3]	<0.0001
aPTT seconds (median [IQR])	37.2 [32.8–42]	36.1 [32.5–40.7]	43.6 [38.4–68.1]	<0.0001
Ferritin μg/l (median [IQR])	335.5 [185.7–779.4]	292.6 [169.2–651.3]	1047.1 [674.8–1483]	<0.0001
Positive PCR for SARS-CoV-2, n (%)	151 (63.2)	122 (59.5)	29 (85.3)	0.004
Treatment
Supplemental oxygen, n (%)	113 (47.3)	110 (53.7)	3 (8.8)	<0.0001
Non-invasive mechanical ventilation, n (%)	5 (2.1)	3 (1.5)	2 (5.9)	0.095
Invasive mechanical ventilation, n (%)	33 (13.8)	2 (0.98)	31 (91.2)	<0.0001
ECMO, n (%)	14 (5.9)	2 (0.98)	12 (35.3)	<0.0001
Renal replacement therapy, n (%)	27 (11.3)	5 (2.44)	22 (64.71)	<0.0001
Medications
Antivirals (total), n (%)	50 (20.92)	27 (13.2)	23 (67.6)	<0.0001
Antibiotics (total), n (%)	226 (94.6)	194 (94.6)	32 (94.1)	0.90
Steroids, n (%)	188 (78.7)	168 (81.9)	20 (58.8)	0.002
Anticoagulation (Oral), n (%)	27 (11.3)	27 (13.2)	0 (0)	0.025
Anticoagulation (IV), n (%)	201 (84.1)	174 (84.9)	27 (79.4)	0.42
Aspirin, n (%)	57 (23.8)	54 (26.3)	3 (8.8)	0.026
Complications/outcome
AKI, n (%)	56 (24.4)	27 (13.2)	29 (85.3)	<0.0001
Direct ICU admission, n (%)	26 (10.9)	0	26 (76.4)	<0.0001
Hospital stay duration, day median [IQR])	8 (6–12)	7 (6–11)	13 (6–21)	0.0032

PCR, polymerase chain reaction; BMI, body mass index; IQR, interquartile range; HbA1, Hemoglobin Subunit Alpha 1; HTN, Hypertension; ALT U/l, Alanine aminotransferase; LDH, U/L, lactate dehydrogenase; CRP, C-reactive protein, INR, International normalized ratio; CT, computerized tomography; AST U/l, Aspartate aminotransferase; aPTT, Activated partial thromboplastin time; ECMO, extracorporeal membrane oxygenation; ICU, Intensive care unit; AKI, acute kidney injury.

Fig 1

Age distribution among survived and deceased patients.

Fig 2

Saturation percentage among survived and deceased patients.

Fig 4

Respiratory rate distribution among survived and deceased patients.

The non-survivors had significantly abnormal complete blood count (CBC) parameters, elevated serum creatinine, urea, aspartate aminotransferase (AST), total bilirubin, total protein and albumin. Similarly, in coagulation profile elevated levels of D-dimer, fibrinogen, international normalized ratio (INR) and activated partial thromboplastin time (aPTT) were more often found among deceased patients compared to survivors. Similar trend was observed for inflammatory markers (ferritin, CRP).

Non-survivors were more likely to need oxygen supplementation, invasive mechanical ventilation, ECMO, to receive antivirals, steroids, aspirin and oral anticoagulation as well as develop acute kidney injury (AKI) compared to survivors.

Association between demographic and clinical data with survival

The crude association between clinical-demographic parameters and risk of death using univariate logistic regression results are summarized in Table 2. Each incremental increase by 5 years of age is associated with 17% higher odds of death (Odds ratio—OR 1.17; 95% confidence interval (CI) 1.01–1.36, p-value (p) = 0.039). Patients with un-controlled diabetes (HbA1c>6.5%) had almost 4-fold higher odds of death (OR 3.96; 95% CI 1.02–15.48; p = 0.048) compared to those, whose HbA1c level was under 6.5%. Each incremental increase by 5 days of later admission is associated with 32% higher risk of death (OR 1.32; 95% CI 1.03–1.68; p = 0.026). Elevated temperature at admission is associated with 2-fold increase odds of death (OR 2.06; 95% CI 1.10–3.83; p = 0.023), each incremental increase by 2 degrees Celsius increase odds of death 4 times. Elevated respiratory rate (RR) (OR 1.23; 95% CI 1.11–1.37; p<0.0001) and reduced oxygen saturation (OR 1.66; 95% CI 1.39–1.99; p<0.0001) also increase odds of death. Each incremental increased of RR by 7 breath/min and each incremental decrease of oxygen saturation by 7% are associated with 4-fold and 3.5-fold risk of death, respectively. Leukocytosis, lymphopenia, anemia, elevated liver and kidney function tests, hypoproteinemia, elevated inflammatory markers (C-reactive protein, ferritin, and LDH) and coagulation tests (fibrinogen, D-dimer, INR, and aPTT) at admission were also associated with higher odds of death in univariate logistic regression analysis (Table 2).

Table 2

The association between demographics, clinical, laboratory, treatment related variable and risk of in-hospital death using unadjusted logistic regression.

Characteristic	number of observations / number of events	Univariate OR (95% CI)	p value
Sex	239 / 34	2.3 (0.99–5.33)	0.052
Age	239 / 34	1.03 (1.00–1.06)	0.039
Age+5		1.17 (1.01–1.36)
BMI	212 / 25	1.05 (0.98–1.14)	0.16
Diabetes	239 / 34	1.57 (0.70–3.53)	0.28
HbA1c > 6.5%	94 / 13	3.96 (1.02–15.48)	0.048
HTN	239 / 34	0.58 (0.28–1.21)	0.15
Cardiovascular disease	239 / 34	0.73 (0.31–1.69)	0.46
Previous cardiac surgery	239 / 34	1.75 (0.79–3.86)	0.17
Day of disease at admission	239 / 34	1.06 (1.01–1.11)	0.026
Day+2		1.12 (1.013–1.23)
Day+5		1.32 (1.034–1.68)
Day+7		1.47 (1.048–2.06)
Day+10		1.73 (1.069–2.81)
Cough	239 / 34	0.71 (0.34–1.47)	0.36
Temperature	220 / 23	2.06 (1.10–3.83)	0.023
Temperature+2		4.23 (1.22–14.68)
Respiratory rate	217 / 15	1.23 (1.11–1.37)	<0.0001
Respiratory rate+5		2.84 (1.69–4.78)
Respiratory rate+7		4.31 (2.08–8.94)
Saturation	140 / 25	0.84 (0.79–0.89)	<0.0001
Saturation-3		1.66 (1.39–1.99)
Saturation-5		2.33 (1.72–3.15)
Saturation-7		3.26 (2.14–4.97)
Polisegmental CT	239 / 34	9.82 (1.31–73.70)	0.026
PCR test	239 / 34	3.95 (1.47–10.61)	0.007
Neutrophils	239 / 34	1.26 (1.16–1.37)	<0.0001
Neutrophils+5		3.22 (2.12–4.88)
Lymphocytes	238 / 33	0.91 (0.87–0.96)	<0.0001
Lymphocytes-5		1.58 (1.26–1.99)
NLR	238 / 33	1.29 (1.11–1.50)	0.001
NLR+5		3.61 (1.69–7.69)
WBC	238 / 33	1.27 (1.16–1.37)	<0.0001
WBC+5		3.18 (2.12–4.78)
PLT	238 / 33	1 (1–1.002)	0.56
RBC	238 / 33	0.44 (0.25–0.77)	0.004
RBC-2		5.26 (1.67–16.5)
HGB	238 / 33	0.97 (0.95–0.99)	0.001
HGB-5		1.16 (1.058–1.26)
HGB-10		1.34 (1.12–1.60)
Urea	238 / 33	1.056 (1.035–1.076)	<0.0001
Urea-5		1.31 (1.19–1.44)
Creatinine	237/33	4.17 (1.89–9.17)	<0.0001
Creatinine+2		17.37 (3.59–84.1)
Total protein	236/33	0.17 (0.086–0.33)	<0.0001
Total protein-2		35.11 (9.15–134.7)
Albumin	197/33	0.12 (0.054–0.27)	<0.0001
Albumin-2		69.46 (13.96–345.49)
Bilirubin	238/33	3.045 (1.64–5.66)	<0.0001
Bilirubin+5		261.89 (11.76–5834.21)
ALT	237 / 33	1.00 (0.99–1.01)	0.41
AST	238 / 33	1.01 (1.01–1.013)	0.027
LDH	195 / 34	1.0039 (1.0012–1.01)	0.005
		1.22 (1.063–1.39)
LDH+50
CRP	233 / 31	1.18 (1.11–1.24)	<0.0001
CRP+5		2.24 (1.70–2.95)
Ferritin	226 / 29	1.002 (1.0014–1.003)	<0.0001
Ferritin+50		1.12 (1.075–1.17)
Ferritin+100		1.26 (1.16–1.36)
GlycHGB	94 / 13	1.13 (0.84–1.54)	0.42
INR	234 / 33	1.60 (1.02–2.53)	0.043
aPTT	238 / 33	1.02 (1.01–1.037)	<0.0001
aPTT+5		1.12 (1.055–1.199)
Fibrinogen	235 / 33	1.3 (1.06–1.59)	0.013
Fibrinogen+5		3.69 (1.32–10.31)
D-dimer	224 / 32	1.39 (1.19–1.62)	<0.0001
D-dimer+5		5.13 (2.36–11.14)
Antivirals,	238/23	13.8 (6.04–31.44)	<0.0001
Antibiotics,	238/32	0.91 (0.19–4.28)	0.902
Corticosteroids,	238/20	0.31 (0.15–0.68)	0.003

BMI, Body mass index; HbA1c, Hemoglobin Subunit Alpha 1; HTN, Hypertension; CT, computerized tomography; PCR, polymerase chain reaction; NLR, Neutrophil-Lymphocyte Ratio; WBC, white blood cell count; PLT, platelet count; RBC, Red blood cell count; HGB, Hemoglobin; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; LDH, Lactate dehydrogenase; CRP, C-reactive protein; GlycHGB, glycosylated hemoglobin; INR, International normalized ratio; aPTT, activated partial thromboplastin time.

After adjustments in different models (Table 3), age (OR 1.20; 95% CI 1.01–1.43; p = 0.035), respiratory rate (OR 1.38; 95% CI 1.07–1.77; p = 0.013) and CRP (OR 1.39; 95% CI 1.04–1.87; p = 0.026) remained as independent factors associated with in-hospital mortality.

Table 3

The association between demographics, clinical, laboratory, treatment related variable and risk of in-hospital death using adjusted logistic regression models.

Covariates	Model 1 OR (95% CI)	p value	Model 2 OR (95% CI)	p value	Model 3 OR (95% CI)	p value
Age	1.07 (1.01–1.13)	0.016	1.09 (1.01–1.19)	0.037	1.2 (1.01–1.43)	0.035
Sex	1.14 (0.34–3.78)	0.827	0.65 (0.11–3.72)	0.63	0.13 (0.01–2.20)	0.16
Disease day	1.03 (0.95–1.12)	0.466	0.99 (1.03–1.39)	0.89	0.81 (0.62–1)	0.14
Respiratory rate	1.22 (1.09–1.37)	<0.001	1.20 (1.03–1.39)	0.018	1.38 (1.07–1.77)	0.013
WBC	-	-	1.15 (1.01–1.31)	0.03	1.30 (0.96–1.76)	0.086
Serum creatinine	-	-	1.3 (0.52–3.27)	0.57	0.80 (0.35–1.80)	0.59
Total protein	-	-	0.41 (0.10–1.73	0.23	0.050 (0.001–1.47)	0.083
CRP	-	-	1.13 (1.00–1.28)	0.043	1.39 (1.04–1.87)	0.026
Fibrinogen	-	-	0.82 (0.50–1.35)	0.44	0.54 (0.22–1.35)	0.19
Hemoglobin	-	-	1.01 (0.96–1.06)	0.72	1.02 (0.97–1.07)	0.41
Antivirals	-	-	-	-	508.5 (5.67–45633)	0.007
Corticosteroid	-	-	-	-	3.35 (0.17–66)	0.43

WBC, white blood cell count; CRP, C-reactive protein.

Discussion

We found that age, respiratory rate and CRP were independent predictors of mortality in our cohort. According to WHO, -“A death due to COVID-19 is defined for surveillance purposes as a death resulting from a clinically compatible illness, in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID disease (e.g. trauma). There should be no period of complete recovery from COVID-19 between illness and death. A death due to COVID-19 may not be attributed to another disease (e.g. cancer) and should be counted independently of preexisting conditions that are suspected of triggering a severe course of COVID-19” [10].

In the face of the COVID-19 pandemic, a number of tertiary hospitals around the globe had to re-organize into the COVID-centers to help with the overflow in the infectious disease centers [11-13]. Similarly, in Kazakhstan, during the peak of pandemic various hospitals were re-organized into COVID centers. One of them was the National Research Cardiac Surgery Center. Often the most severe cases and critically ill patients were referred to our hospital, which has sufficient experience and equipment to provide advanced floor and ICU care. This could explain why the case fatality rate in our study 3–4 folds was higher than nationwide or worldwide COVID-19 fatality rate of 3–4% [2,13].

Previous studies showed that older age is associated with increased risk of mortality [4,14]. This is consistent with findings from our study, where older age was found to be an independent predictor of mortality from COVID-19. Literature review also showed that male sex was reported to be associated with increased in-hospital mortality as well as ICU admissions [14,15]. In this study, even though non-survivors were more likely to be males, logistic regression failed to reveal significant difference possibly due to a low sample number.

This study found that the day of disease from the onset of symptoms to admission was associated with higher odds of death, which is supported by evidence from previous research [5]. Importantly, we also confirmed that the longer the waiting time, the higher the odds of non-surviving, which emphasizes the importance of early diagnosis, observation and triage of patients in order to prevent unfavorable outcome.

In this study it was founded that reduced oxygen saturation, elevated body temperature, respiratory rate and degree of respiratory failure are associated with higher odds of death. Moreover, respiratory rate was concluded to be an independent predictor of mortality. Consistently, severity of the disease, which is judged upon the abnormality of these clinical symptoms, was also associated with increased odds of death. These findings are consistent with previously reported [2]. It is important to note that these characteristics in general are easily obtained in a hospital or outpatient, and if abnormal should prompt further testing to make a decision regarding hospital admission.

Previous studies report that presence of comorbidities specifically cardiovascular diseases, hypertension and diabetes are associated with increased risk of death from COVID-19 [14,16,17]. The results of this study showed no association between these variables. Importantly, however, this study showed that elevated glycated hemoglobin level was associated with increased odds of death. This could be explained by the presence of uncontrolled and/or previously undiagnosed diabetes in the patients from our cohort. We hence propose that level of glycated hemoglobin should be assessed upon admission for patients at increased risk of diabetes, despite not previously documented diagnosis. Although limited by missing data, this is an important finding that should be studied more carefully.

This study results support previously reported findings [5,18,19] that the baseline laboratory characteristics could serve as predictors of mortality. Specifically, we concluded that elevated white blood cell count as well as elevated CRP are independent predictors of mortality in our cohort. Other laboratory findings in complete blood count, biochemical, hematological and inflammatory profiles that were found to be more likely abnormal in non-survivors in our cohort are consistent with previously reported data [2,5,18-20].

Although limited by number of patients in the cohort, we showed that non-survivors were more likely to require supplemental oxygen and advanced ICU care with intubation, renal replacement therapy and ECMO. Non-survivors also more often were treated with antivirals. Acute kidney injury was more likely to develop in non-survivors. These are in consistency with previously reported findings [21].

Based on previous studies according to Giovanni et all [24], which is described the work Qin et al. [22] we analyzed markers, in particular, white blood cells and CRP in a cohort of 450 patients with confirmed COVID-19, and found that the severe course of the disease is characterized by an increased level of white blood cells and an increase in C-reactive protein, compared with patients with a moderate course. Similarly, in other works, for example, in Henry et al. It was also concluded in a meta-analysis of 21 studies involving 3377 patients with confirmed COVID19 that patients with a severe course of the disease and fatal outcomes had very high levels of white blood cells, compared with patients with a mild course of the disease and recovered patients. [23,24] What we observed in our patients as well.

This study is limited by the small sample size, missing data for some variables and high proportion of cases not confirmed by PCR. Recent study from Kazakhstan also reported the 85% of SARS-CoV-2 PCR negative cases of pneumonia during the peak of COVID-19 infection [25]. In addition, the study was conducted in a very specialized tertiary hospital, which limits generalizability of the results. Patients who had diet most like to received antivirals, which was the single option to prescribe them at that time and results indicated it as a significant associated factor with mortality. However, this effect is potential to reverses epidemiology due to treatment bias.

Conclusion

In conclusion, this is the first study assessing mortality of COVID-19 patients from the tertiary hospital in Kazakhstan. This study describes 14% in-hospital mortality from COVID-19. Many abnormal clinical and laboratory data at admission associated with poor outcome. Age, respiratory rate and CRP were found to be independent predictors of mortality. The findings of this study could help with triage of such patients in order to avoid overload of healthcare facilities and limit involvement of highly specialized centers to cases that are more likely to develop severe disease course. Our finding would help healthcare providers to predict the risk factors associated with high risk of mortality. Further investigations involving large cohorts should be provided to support our findings.

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

7 Sep 2021

PONE-D-21-25018Mortality predictors of hospitalized patients with COVID-19:Retrospective Cohort Study from Nur-Sultan, KazakhstanPLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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5. Review Comments to the Author

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Reviewer #1: Mortality predictors of hospitalized patients with COVID-19:Retrospective Cohort Study

from Nur-Sultan, Kazakhstan

The study has merits, and important for understanding the characteristics of patient who are likely to die from COVID-19 and institute steps to avert that. However, I do have some concerns that need to be resolved;

1. Ethical statement: preliminary information section stated that “The study was approved by the Institutional Review Ethics Committee of the National Research Cardiac Surgery Center (#01-97/2021 from 22/04/21). The written consent was obtained”

a. Was wondering how written consent was obtained in a retrospective study

b. However contradicting information was found on, Page 7, lines 177-178 that stated “Therefore this study was exempt from informed consent acquisition process.”

2. Page 4, lines 109- 111; described the inclusion criteria “Diagnosis of COVID-19 was defined as either a positive nasopharyngeal swab for SARS-CoV-2 by reverse transcriptase polymerase chainmreaction (PCR) and/or clinically supported evidence of SARS-CoV-2 in the absence of PCR confirmed test”

a. “The clinically supported evidence of SARS-CoV-2 in the absence of PCR confirmed test” needs further clarity.

i. What clinical criteria was used?

ii. The criteria above suggests that the presence of “clinical symptoms” in a PCR negative person still qualifies the person for classification as COVID-19 case in this study

3. Page 6, lines 132- 134 “……antibiotics (Cefuroxim, Cefazolin, Ceftriaxon, Cefepim. Moxifloxacin, Aztreonam. Meropinem. Levafloxacin. Fluconazole), anticoagulation and steroids were recorded”

a. The phrase needs revising, the multiple full stops need to be checked

b. Are authors suggesting that Fluconazole is an antibiotic

4. Page 6, study definitions: lines 138 – 142 stated the criteria for disease severity:

“Severity of the disease was evaluated based on clinical symptoms in accordance with national clinical guidelines. “The disease severity was stratified as: mild disease – body temperature <38°С, heart rate <90 bpm, respiratory rate <24/min; moderate disease – body temperature 38.1-39°С, heart rate 90-120 bpm, respiratory rate > 24/min; and severe disease – high temperature (over 39°С) with severe symptoms (headache, myalgia, nausea), heart rate > 120 bpm, respiratory rate >28/min.

a. I appreciate your national guidelines but for the purpose or research and uniformity in comparing several studies across many countries, I would advise you use globally acceptable severity classification; eg WHO classification of COVID-19 severity

b. Also, your classification based on temperature and clinical features described as “severe symptoms (headache, myalgia, nausea)” may lead to misclassification bias;

i. The clinical features described as severe symptoms are very subjective depending on the pain threshold of patients etc

ii. Also, it is known that not all patients with COVID-19 report high grade fever, hence using fever and some other subjective symptoms as criteria for measuring severity needs further explanation.

5. Page 7, lines 174-175 reads “The outpatient cards of patients are presented by anonymized database without any identifiable information about patients.”

a. The meaning is not clear

b. Also, “outpatient cards” a little confusing; I though our study participants were “inpatients”

6. Page 8, results section; line 185 states; “151 patients (63.2%) had PCR confirmed COVID-19 diagnosis.

a. The 54 who did not have a COVID-19 diagnosis, what disease condition were there suffering from

b. Since your title, and objective is to describe “Mortality predictors of hospitalized patients with COVID-19: Retrospective Cohort Study from Nur-Sultan, Kazakhstan”

i. I find it difficult to understand why other patients for which a diagnosis of COVID-19 was not confirmed were included.

ii. The symptoms of COVID-19 symptoms are really similar to that of various other infectious diseases, and it is possible most of the 54 without a confirmed PCR diagnosis were actually not COVID-19 cases. Though it was mentioned in the limitations, I feel it is a fundamental issue that affects the estimates made and, that need to be looked at again.

Reviewer #2: General

• The paper needs to be reviewed by native English speaker

Abstract

• Line 46: Do not write numbers in figure at the beginning of any sentence.

• Line 55: Is it discussion or conclusion?

• Line 56 – 58: You need to put recommendations and/or policy/strategic options for your findings. Otherwise, the findings may not have much relevance.

Methods

• Lines 107 – 111: Is it possible to diagnose COVID-19 clinically? It has similar sign and symptoms with other respiratory diseases including common cold. Do you think that it doesn’t affect your findings?

• Describe number of participants, inclusion and exclusion and ethical statement separately. It is not part of the statistical analysis.

• In this part, you should indicate that how did you ascertain mortality due to COVID-19. How could you be sure that the deaths are not due to other medical conditions? Death definition for COVID-19 is required.

Result

• Line 185: Do not write numbers in figure at the beginning of any sentence. Write it in word.

• Lines 189 – 191: The narration here looks conclusion or discussion. Here, the author should narrate the actual value (avoid using of more, most, more likely….)

• In Table 1: Under the variable gender, include also other categories like female or other categories if any like you for obesity in the same table. This table also need footnote, there so many abbreviations and symbols in the table. These should be described/stated in footnote.

• Lines 194 – 200: Is it the appropriate place for the list of figure titles?

• Lines 202 – 208: Similar comment with lines 189 – 191.

• Tables 2 & 3 need footnote as well.

• All figures also need footnotes and abbreviations in the lend need to stated there.

Discussion

• I don’t think that the first sentence of the discussion is relevant.

• Lines 239 – 244: This is stated also in the introduction part. I think these statements are better in the introduction. These are not your findings/I haven’t found them in the result part. Therefore, my recommendation here is to remove from the discussion and keep in the introduction part.

• Lines 275 – 280: The authors stated that the derangements of laboratory findings are predictors of mortality. This is very interesting. However, you need to explain why, what are the implications here.

• Lines 291 – 297: Conclusion should be a separate heading and, in this part, put your recommendations and policy/strategic directions other than putting the importance of the findings.

Reviewer #3: 1. Line 229 - 231 stated that there are 3 independent factors associated with in-hospital mortality: ag, respiratory rate and CRP. In Table 3, Antiviral has p-value 0.007. Please clarify why it is not considered as one of the independent factors to in-hospital mortality.

2. Are those with diagnosis not confirmed with PCR taken during initial presentation had their PCR re-taken during the course of their admission? Is there a reason why these patients considered to be Covid-19 when there are no positive result?

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Nurul Huda Ahmad

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7 Oct 2021

Answers to the review Comments to the Author

Reviewer #1:

2. During the internal evaluation of the manuscript we have noted a discrepant in the informed consent procedure reported. For instance in the ethics statement on the online submission form, it is stated that informed consent was obtained from participants, however within the manuscript text, it is reported that informed consent was exempt. Please could you provide some further clarification on this.

- Ethical statement: preliminary information section stated that “The study was approved by the Institutional Review Ethics Committee of the National Research Cardiac Surgery Center (#01-97/2021 from 22/04/21). The written consent was obtained”

-Answer: We agree with your comments and sorry for this technical mistake. Since the study is retrospective and exemption from informed consent approved by ethic committee decision.

- 2. Page 4, lines 109- 111; described the inclusion criteria “Diagnosis of COVID-19 was defined as either a positive nasopharyngeal swab for SARS-CoV-2 by reverse transcriptase polymerase chainmreaction (PCR) and/or clinically supported evidence of SARS-CoV-2 in the absence of PCR confirmed test”

a. “The clinically supported evidence of SARS-CoV-2 in the absence of PCR confirmed test” needs further clarity.

I. What clinical criteria was used?

-Answer: The main clinical criterion is: COVID-19-associated pneumonia. Based on the established diagnosis of pneumonia according to CT data, where the leading signs in the early stages of the disease were foci of "frosted glass", multifocality of lung damage, edema of the interalveolar pulmonary interstitium, which was the difference between COVID-19-associated pneumonia from that of another etiology.

In addition to pneumonia, the following clinical criteria were taken into account: close contact with infected patients, clinical signs of COVID-19 which are available elsewhere.

II. The criteria above suggests that the presence of “clinical symptoms” in a PCR negative person still qualifies the person for classification as COVID-19 case in this study

- Answer: Patients with a negative PCR test, but with clinical signs of COVID-19, such as pneumonia (confirmed by chest CT), were selected for the study in accordance with the WHO International Classification. The code U07. 2 is COVID-19, where the COVID-19 virus has not been identified, diagnosed clinically or epidemiologically, but laboratory studies are inconclusive or unavailable. https://icd.who.int/browse10/2019/en#/U07.2

3. Page 6, lines 132- 134 “……antibiotics (Cefuroxim, Cefazolin, Ceftriaxon, Cefepim. Moxifloxacin, Aztreonam. Meropinem. Levafloxacin. Fluconazole), anticoagulation and steroids were recorded”

a. The phrase needs revising, the multiple full stops need to be checked

- Answer: done!

b. Are authors suggesting that Fluconazole is an antibiotic

- Answer: We agree that fluconazole is not an antibiotic, this is an antimicrobial drug. Therefore, we have corrected this point.

-Page 6, study definitions: lines 138 – 142 stated the criteria for disease severity:

“Severity of the disease was evaluated based on clinical symptoms in accordance with national clinical guidelines. “The disease severity was stratified as: mild disease – body temperature <38°С, heart rate <90 bpm, respiratory rate <24/min; moderate disease – body temperature 38.1-39°С, heart rate 90-120 bpm, respiratory rate > 24/min; and severe disease – high temperature (over 39°С) with severe symptoms (headache, myalgia, nausea), heart rate > 120 bpm, respiratory rate >28/min.

a. I appreciate your national guidelines but for the purpose or research and uniformity in comparing several studies across many countries, I would advise you use globally acceptable severity classification; eg WHO classification of COVID-19 severity

Answer: Actually we have followed the WHO classification of the disease severity, but it seems our definition in the manuscript was not clear. Our national guidelines were also fully adapted and written on the basis of Clinical management, WHO, Living guidance, 25 January 2021. https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-1 ).

Also, your classification based on temperature and clinical features described as “severe symptoms (headache, myalgia, nausea)” may lead to misclassification bias;

Answer: We agree with you, perhaps these symptoms can be attributed to general non-specific symptoms. But in the case of our study, we assessed the severity of the patients ' condition in conjunction with objective data such as the development of COVID-associated pneumonia according to CT results, signs of respiratory inaccuracy (decreased oxygen saturation) as well as laboratory biomarkers of severity: increases in the level of C reactive protein, ferritin, d-dimmer.

The clinical features described as severe symptoms are very subjective depending on the pain threshold of patients etc

Answer: The severity of the patients ' condition is assessed by objective signs: laboratory data, instrumental data (CT and X-ray), the degree of lung damage, biochemical indicators, the presence of comorbid diseases.

i. Also, it is known that not all patients with COVID-19 report high grade fever, hence using fever and some other subjective symptoms as criteria for measuring severity needs further explanation.

Answer: The body temperature of patients was measured starting from the emergency room and in the department in dynamics several times by medical personnel.

5. Page 7, lines 174-175 reads “The outpatient cards of patients are presented by anonymized database without any identifiable information about patients.”

a. The meaning is not clear

Answer: During the data collection, all personal information of patients is encoded and the data is depersonalized, so we protect the rights of patients and do not disclose their personal information. We have little clarified this sentence in the manuscript

b. Also, “outpatient cards” a little confusing; I though our study participants were “inpatients”

Answer: We agree with the remark, we changed it to medical records (medical report). All the patients were on inpatient treatment. We apologize for this mistyping

6. Page 8, results section; line 185 states; “151 patients (63.2%) had PCR confirmed COVID-19 diagnosis.

a. The 54 who did not have a COVID-19 diagnosis, what disease condition were there suffering from

Answer: Actually, all patients had COVID-19 disease with or without PCR positivity. From the cohort, 88 patients (not 54!) presented with PCR negative COVID-19 disease (virus not identified coronavirus pneumonia).

b. Since your title, and objective is to describe “Mortality predictors of hospitalized patients with COVID-19: Retrospective Cohort Study from Nur-Sultan, Kazakhstan”

i. I find it difficult to understand why other patients for which a diagnosis of COVID-19 was not confirmed were included.

Answer: We apologize for this confusion. The text states that the diagnosis of COVID-19 has not been confirmed on the basis of PCR for the rest of the patients, but the diagnosis is made on such a clinical criterion as pneumonia. In clinical practice, there are many cases when PCR is not convincing in patients and therefore the WHO classification has the ICD-10 code U07.2 (COVID-19, virus not identified).

ii. The symptoms of COVID-19 symptoms are really similar to that of various other infectious diseases, and it is possible most of the 54 without a confirmed PCR diagnosis were actually not COVID-19 cases. Though it was mentioned in the limitations, I feel it is a fundamental issue that affects the estimates made and, that need to be looked at again.

Answer: We agree with you that the symptoms of COVID-19 are similar to other respiratory infections. But as we indicated above, in 88 patients (not 54 patients) who had negative PCR results, the diagnosis of COVID-19 was established on the basis of CT- characteristic of COVID-19 pneumonia and other biomarkers such as: increased CRP, ferritin, d-dimer. Our experience was based on WHO clinical guidelines and other literature data.

Reviewer #2: General

• The paper needs to be reviewed by native English speaker

Answer: The manuscript was revised by native English speaker and some correction were made through the text

Abstract

• Line 46: Do not write numbers in figure at the beginning of any sentence.

Answer: The remark is eliminated

• Line 55: Is it discussion or conclusion?

Answer: The remark is eliminated. This section means the conclusion.

• Line 56 – 58: You need to put recommendations and/or policy/strategic options for your findings. Otherwise, the findings may not have much relevance.

Answer: we thank reviewer for this recommendation. We provided the suggested recommendation in conclusions . “Our finding would help healthcare providers to predict the risk factors associated with high risk of mortality. Further investigations involving large cohorts should be provided to support our findings.”

Methods

• Lines 107 – 111: Is it possible to diagnose COVID-19 clinically? It has similar sign and symptoms with other respiratory diseases including common cold. Do you think that it doesn’t affect your findings?

Answer: To confirm the diagnosis, we used Clinical management, WHO, Living guidance, 25 January 2021 ( https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-1) Similar comments responded for Reviewer 1 (please see above)

-Describe number of participants, inclusion and exclusion and ethical statement separately. It is not part of the statistical analysis.

Answer: No patients were excluded from the study, all 239 patients who were admitted to the COVID-19 department during the June 16th – August 30th period were included to the study. We mentioned this in the section “Study Design and Participants”. The ethical statement transferred to the appropriate section.

-In this part, you should indicate that how did you ascertain mortality due to COVID-19. How could you be sure that the deaths are not due to other medical conditions? Death definition for COVID-19 is required.

Answer: We have established the hospital mortality rate from COVID-19 according to the generally accepted formula: the number of patients who died from COVID-19 x 100/ the number of patients discharged with COVID-19. We are sure that the death occurred from COVID-19, and not from other diseases, since the hospital conducts a clinical analysis of fatal cases together with doctors and the department of treatment quality and patient safety. Also, every patient hospitalized with a diagnosis of COVID-19 was thoroughly examined according to WHO international recommendations.

According to “International guidelines for certification and classification (coding) of covid-19 as cause of death” from WHO /https://www.who.int/classifications/icd/Guidelines_Cause_of_Death_COVID-19.pdf/

A death due to COVID-19 is defined for surveillance purposes as a death resulting from a clinically compatible illness, in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID disease (e.g. trauma). There should be no period of complete recovery from COVID-19 between illness and death. A death due to COVID-19 may not be attributed to another disease (e.g. cancer) and should be counted independently of preexisting conditions that are suspected of triggering a severe course of COVID-19.

Result

• Line 185: Do not write numbers in figure at the beginning of any sentence. Write it in word.

Answer: The remark is eliminated

• Lines 189 – 191: The narration here looks conclusion or discussion. Here, the author should narrate the actual value (avoid using of more, most, more likely….)

Answer: The remark is eliminated

• In Table 1: Under the variable gender, include also other categories like female or other categories if any like you for obesity in the same table. This table also need footnote, there so many abbreviations and symbols in the table. These should be described/stated in footnote.

Answer: comments and suggestions were made

• Lines 194 – 200: Is it the appropriate place for the list of figure titles?

Answer: This is written according to the requirements of the PlosOne guide for authors. Below we give excerpts from the instructions to the authors

«Figures

Do not include figures in the main manuscript file. Each figure must be prepared and submitted as an individual file.

Figure captions

Figure captions must be inserted in the text of the manuscript, immediately following the paragraph in which the figure is first cited (read order). Do not include captions as part of the figure files themselves or submit them in a separate document.

At a minimum, include the following in your figure captions:

A figure label with Arabic numerals, and “Figure” abbreviated to “Fig” (e.g. Fig 1, Fig 2, Fig 3, etc). Match the label of your figure with the name of the file uploaded at submission (e.g. a figure citation of “Fig 1” must refer to a figure file named “Fig1.tif”).

A concise, descriptive title

The caption may also include a legend as needed.

Cite figures in ascending numeric order at first appearance in the manuscript file.»

• Lines 202 – 208: Similar comment with lines 189 – 191.

Answer: This is written according to the requirements of the PlosOne guide for authors

• Tables 2 & 3 need footnote as well.

Answer: The footnote is put under the tables 2 and 3

• All figures also need footnotes and abbreviations in the lend need to stated there.

Answer: It’s unacceptable according to the requirements of the PlosOne guide for authors

Discussion

• I don’t think that the first sentence of the discussion is relevant.

Answer: We have removed this sentence

• Lines 239 – 244: This is stated also in the introduction part. I think these statements are better in the introduction. These are not your findings/I haven’t found them in the result part. Therefore, my recommendation here is to remove from the discussion and keep in the introduction part.

Answer: We removed these lines from Discussion part and inserted to the Introduction

• Lines 275 – 280: The authors stated that the derangements of laboratory findings are predictors of mortality. This is very interesting. However, you need to explain why, what are the implications here.

Answer: Based on previous studies, for example, Qin et al. We analyzed markers, in particular, white blood cells and CRP in a cohort of 450 patients with confirmed COVID-19, and found that the severe course of the disease is characterized by an increased level of white blood cells and an increase in C-reactive protein, compared with patients with a moderate course. Similarly, in other works, for example, in Henry et al. It was also concluded in a meta-analysis of 21 studies involving 3377 patients with confirmed COVID19 that patients with a severe course of the disease and fatal outcomes had very high levels of white blood cells, compared with patients with a mild course of the disease and recovered patients. What we observed in our patients as well

Source: Biomarkers associated with COVID-19 disease progression Giovanni Ponti, Monia Maccaferri, Cristel Ruini, Aldo Tomasi, Tomris Ozben Crit Rev Clin Lab Sci. 2020 : 1–11. Published online 2020 Jun 5. doi: 10.1080/10408363.2020.1770685 PMCID: PMC7284147

• Lines 291 – 297: Conclusion should be a separate heading and, in this part, put your recommendations and policy/strategic directions other than putting the importance of the findings.

Answer: We provided the recommendation policy in conclusions. (Our finding would help healthcare providers to predict the risk factors associated with high risk of mortality. Further investigations involving large cohorts should be provided to support our findings)

Reviewer #3:

1. Line 229 - 231 stated that there are 3 independent factors associated with in-hospital mortality: ag, respiratory rate and CRP. In Table 3, Antiviral has p-value 0.007. Please clarify why it is not considered as one of the independent factors to in-hospital mortality. Answer: because of reverse epidemiology effect (possible to treatment bias) we could not comment on this. All patients who were critically ill had received antivirals (it was single option to prescribe them). This is resulted that most of the critically ill patients died who received antivirals. We provided this information in the limitations.

2. Are those with diagnosis not confirmed with PCR taken during initial presentation had their PCR re-taken during the course of their admission? Is there a reason why these patients considered to be Covid-19 when there are no positive result?

Answer: The PCR test was performed twice. Patients with a negative PCR test, but with clinical signs of COVID-19, such as pneumonia, were selected for the study in accordance with the WHO International Classification. The code U07. 2 is COVID-19, where the COVID-19 virus has not been identified, diagnosed clinically or epidemiologically, but laboratory studies are inconclusive or unavailable. https://icd.who.int/browse10/2019/en#/U07.2

Submitted filename: Response to Reviewers PLOS One2.docx

Click here for additional data file.

21 Oct 2021

PONE-D-21-25018R1

Mortality predictors of hospitalized patients with COVID-19:Retrospective Cohort Study from Nur-Sultan, Kazakhstan

PLOS ONE

Dear Dr. Sailybayeva,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Dec 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Muhammad Tarek Abdel Ghafar, M.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

1- Please check and ensure that all abbreviations in the text are fully defined in full terms before they are first used in the text.

2- Figures 1, 2, 3, and 4 should be presented as bar graphs with error bars.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed all my concerns raised in the first round of the review. I do not have any further concerns.

Reviewer #2: (No Response)

Reviewer #3: (No Response)

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Nurul Huda Ahmad

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

14 Nov 2021

- All abbreviations in the text are fully defined in full terms before they are first used in the text.

- Figures 1, 2, 3, and 4 are presented as bar graphs with error bars

Submitted filename: Response to Reviewers PLOS One2.docx

Click here for additional data file.

22 Nov 2021

PONE-D-21-25018R2Mortality predictors of hospitalized patients with COVID-19:Retrospective Cohort Study from Nur-Sultan, KazakhstanPLOS ONE

Dear Dr. Sailybayeva,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Jan 06 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Muhammad Tarek Abdel Ghafar, M.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

The type of graphs is mentioned as bar graphs with error which is not correct. The correct is Box plot with whiskers. Please revise and correct.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

25 Nov 2021

Dear Managing Editor!

We would like to express our deepest gratitude for the time and efforts of the Reviewers and the Editor spent on our manuscript. All the outstanding comments enlightened us, and they will ultimately improve the scientific and practical content of our paper.

We are resubmitting the revised version that incorporates the suggestions made by the and feel that the manuscript is greatly enhanced as a result.

Point to point answers to the review Comments to the Author

- Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Answer: The retracted article from [Giovanni Ponti, Monia Maccaferri, Cristel Ruini, Aldo Tomasi, Tomris Ozben. Biomarkers associated with COVID-19 disease progression. Crit Rev Clin Lab Sci. 2020 : 1–11. Published online 2020 Jun 5. doi: 10.1080/10408363.2020.1770685] is included with original citations and the Reference list is filled by references from retracted article.

Based on previous studies according to Giovanni et all [24], which is described the work Qin et al. [22] we analyzed markers, in particular, white blood cells and CRP in a cohort of 450 patients with confirmed COVID-19, and found that the severe course of the disease is characterized by an increased level of white blood cells and an increase in C-reactive protein, compared with patients with a moderate course. Similarly, in other works, for example, in Henry et al. It was also concluded in a meta-analysis of 21 studies involving 3377 patients with confirmed COVID19 that patients with a severe course of the disease and fatal outcomes had very high levels of white blood cells, compared with patients with a mild course of the disease and recovered patients. [23, 24] What we observed in our patients as well.

References

22. Qin C, Zhou L, Hu Z, et al. Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis. 2020. DOI:10.1093/cid/ciaa248

23. Henry BM, de Oliveira MHS, Benoit S, et al. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med. 2020.

24. Giovanni Ponti, Monia Maccaferri, Cristel Ruini, Aldo Tomasi, Tomris Ozben. Biomarkers associated with COVID-19 disease progression. Crit Rev Clin Lab Sci. 2020 : 1–11. Published online 2020 Jun 5. doi: 10.1080/10408363.2020.1770685

-The bar graphs with error are replaced with the Box plot with whisker.

We hope that our manuscript in its current revised format is now suitable for publication in PLOS ONE

We look forward to hearing your decision.

Sincerely, Aliya Sailybaeva

Head of Research department of “National Research Cardiac Surgery Centre” JSC, 010000, Republic of Kazakhstan, Nur-Sultan, Turan Avenue 38, phone number: +77172703153, fax number: +7 (7172) 703 158, dr.alisai@gmail.com, cardiacsurgeryres@gmail.com

Submitted filename: Response to Reviewers PLOS One2.docx

Click here for additional data file.

26 Nov 2021

Mortality predictors of hospitalized patients with COVID-19:Retrospective Cohort Study from Nur-Sultan, Kazakhstan

PONE-D-21-25018R3

Dear Dr. Sailybayeva,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Muhammad Tarek Abdel Ghafar, M.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

6 Dec 2021

PONE-D-21-25018R3

Mortality predictors of hospitalized patients with COVID-19: Retrospective Cohort Study from Nur-Sultan, Kazakhstan

Dear Dr. Sailybayeva:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof Muhammad Tarek Abdel Ghafar

Academic Editor

PLOS ONE