Tuberc Respir Dis > Volume 83(Supple 1); 2020 > Article
Cortés-Tellés, López-Romero, Mancilla-Ceballos, Ortíz-Farías, Núñez-Caamal, and Figueroa-Hurtado: Risk Factors for Mortality in Hospitalized Patients with COVID-19: An Overview in a Mexican Population

Abstract

Background

Currently, Mexico ranks third worldwide in mortality due to coronavirus disease pandemic 2019 (COVID-19) and reliable information is scarce, with the available data focused on epidemiological characteristics. This study aimed to identify the risk factors associated with mortality and outcomes in hospitalized Mexican patients with COVID-19.

Methods

We prospectively assessed patients admitted to a COVID-19 reference center in southeast Mexico between March 28 and June 30, 2020. Mortality was defined as survivors or non-survivors and univariate and multivariate logistic regression analyses were performed to explore the association of the clinical characteristics and laboratory parameters with mortality.

Results

We included 200 patients with a mean age of 55 years, 69% were men and 72% had at least one chronic comorbidity. Eighty-six patients required invasive mechanical ventilation (IMV) with an overall mortality rate of 82.5%. Only 51% of the patients with IMV were admitted to the intensive care unit (ICU), with a survival rate of 27.3%, but only 7.2% for patients without ICU admissions (p=0.014). The multivariate analysis found that a neutrophil-to-lymphocyte ratio ≥9 (odds ratio [OR], 4.64; 95% confidence interval [CI], 2.05-10.53) albumin <3.5 g/dL (OR, 3.76; 95% CI, 1.56-9.07), lactate dehydrogenase (LDH) level ≥725 U/L (OR, 5.45; 95% CI, 2.36-12.57), and IMV (OR, 64.7; 95% CI, 15.20-275.39) were independent risk factors associated with mortality.

Conclusion

Neutrophil-to-lymphocyte ratio, LDH, albumin, and IMV were independent risk factors for mortality in Mexican patients with COVID-19. Also, the availability of ICU resources is invaluable for better outcomes in critically ill patients. Our results could provide clinical information for timely decision-making in low-and-middle income countries to overcome the pandemic.

Introduction

The coronavirus disease pandemic of 2019 (COVID-19) continues to spread globally, causing in the deaths of more than half a million people worldwide and unprecedented economic impact. So far, it has been recognized that advanced age, chronic diseases [1-3], and several laboratory result abnormalities [4] are associated with severe and fatal forms of COVID-19. Current pandemic concerns are centered in The Americas where, according to a report by Johns Hopkins University [5] together with the World Health Organization (WHO), the United States, Brazil, and Mexico have ranked among the top five countries in the world since July for the highest number of confirmed COVID-19 deaths. Currently, Mexico holds the third place for COVID-19 deaths worldwide [6].
Mexico’s adult population has a higher prevalence of several chronic medical conditions including overweight status and obesity (72.5%) [7], hypertension (25.5%) [8], and diabetes mellitus (13.7%) [9]. Previous studies have identified these chronic medical conditions as risk factors associated with severe disease and mortality in COVID-19 patients [10].
Since early July, Mexico has faced the epidemiological phase of greatest community transmission as the nation’s economic activity resumed, resulting in a significant increase in the number of daily cases and tripling COVID-19 mortality in our territory [11]. Reliable information on our population is scant and available data has focused only on epidemiological characteristics. There is an urgent need for clinical data to support decision-making in the medical management and availability of intensive care unit (ICU) resources related to the high number of deaths.
The available information of the mortality risk factors and outcomes in hospitalized patients with COVID-19 in the Mexican population so far is scarce. To address this knowledge gap, we conducted a study that examined the clinical characteristics and laboratory parameters to analyzed the mortality risk factors and outcomes the the first 200 patients admitted to a COVID-19 hospital reference center in southeast Mexico.

Materials and Methods

1. Study design and patients

This single-center, observational, ambispective cohort study was conducted at the Hospital Regional de Alta Especialidad de la Peninsula de Yucatan, a third-level reference center currently designated to evaluate and treat patients with COVID-19. All adult patients who were admitted with an acute respiratory illness (fever, cough, dyspnea) and were diagnosed with COVID-19 based on the WHO interim guidance from March 28 to June 30, 2020 were included in our study [12]. The protocol was approved by the local research ethics committee (2020-023). All patients signed informed consent on admission and the information was kept strictly confidential under the guidelines established in the Declaration of Helsinki [13].

2. Data collection

The information of all patients, including demographics, clinical presentation, laboratory parameters and patient outcomes were extracted from medical records. Two researchers independently reviewed the information to double-check the collected data. If data were missing or unclear, the attending physicians were interviewed for clarification. We collected data on age, sex, medical history, and symptoms from onset to hospital admission. Laboratory assessments consisted of a complete blood count, blood chemistry analysis, coagulation profile, D-dimer, assessment of liver, renal and cardiac function, electrolyte measurements, lactate dehydrogenase, creatine kinase (CK), CK-MB, troponin T, and inflammatory biomarkers, as C-reactive protein, erythrocyte sedimentation rate, procalcitonin, and serum ferritin. Laboratory parameters were divided into three categories: hematologic and inflammatory biomarkers, coagulation factors and organ-injury biochemical biomarkers [4]. The primary outcome was mortality defined as survivor or non-survivor at the time of the analysis. Independent predicted factors associated with fatal outcomes were analyzed as secondary outcomes.
On admission, nasal and pharyngeal swabs specimens were sent to the State Public Health Laboratory for processing (Laboratorio Estatal de Referencia Epidemiologica del Estado de Yucatan). All specimens were tested for COVID-19 with real-time reverse-transcriptase-polymerase chain reaction based on the protocol established by the WHO [14,15].

3. Statistical analysis

We expressed descriptive data as median (interquartile range, IQR) for continuous variables and number (%) for categorical variables. We assessed differences between non-survivors and survivors using Wilcoxon’s rank-sum test for continuous variables and chi-square test or Fisher exact test for categorical variables. Univariate and multivariate logistic regression analysis were performed to explore the association of clinical characteristics and laboratory parameters (inflammation-related indices and organ-injured related biomarkers) with mortality (dependent variable). Tests were two-sided with significance set at α less than 5%. The STATA version 13 software (Stata Corp., College Station, TX, USA) was applied for all analysis.

Results

1. Baseline characteristics of the study population

We included the first 200 cases with complete data who were followed up and had an outcome until June 30, 2020. Descriptive characteristics are detailed in Table 1. The median age was 55 years (IQR, 41-65), 55% of whom were older than 65 years and 138 (69%) were male. In our cohort, 144 patients (72%) have one or more chronic medical conditions, obesity being the most prevalent (52%), followed by hypertension (30%) and diabetes mellitus (28%). The median duration from symptoms onset to baseline evaluation was 8 days (IQR, 6-9). On admission, most common symptoms were fever (96%), dyspnea (93%), and cough (90%), with oxygen saturation 87% on room air (IQR, 78%-90%), pulse oximetric saturation/fraction of inspired oxygen (SpO2/FiO2) of 414 (371-431), and quick Sequential Organ Failure Assessment (qSOFA) score of 1 point (IQR, 1-1). Overall, the need of invasive mechanical ventilation (IMV) was 43% of all hospitalized patients with an overall mortality rate of 82.5%. Only the 51% of patients with IMV received attention in ICU, 32 patients (72.7%) died in a ICU vs 39 patients (92.7%) who died in a different area of the hospital. The median of hospital stay was 6 days (4-12 days) (Table 1).

2. Clinical and epidemiological differences between non-survivors and survivors

Compared to survivors, non-survivors of COVID-19 represented the 38.7% of the entire cohort with a higher proportion of patients older than 65 years (43% vs. 18%, p<0.001) (Table 1).
There were non-significant differences between groups in chronic medical conditions and symptoms, nonetheless, intensity of dyspnea on admission among non-survivors was higher considering modified Medical Research Council (mMRC) scale (3 vs. 2, p<0.001). Compared to survivors, non-survivors had a significantly higher heart rate (107 vs. 98, p=0.022) and respiratory rate (30 vs. 28, p=0.003), lower baseline SpO2 (73% vs. 89%, p<0.001) and SpO2/FiO2 (348 vs. 424, p<0.001). Also, mortality in non-survivors who did not received attention on an ICU were higher (92.8% vs. 72.7%, p=0.014).

3. Laboratory indices differences between non-survivors and survivors

Substantial differences in laboratory findings between non-survivors and survivors are summarized in Table 2. Compared with survivors, levels of hematological and inflammationrelated indices including leukocytosis (13.1 vs. 9.4, p<0.001), neutrophil-to-lymphocyte ratio (NLR; 12.3 vs. 6.3, p<0.001), procalcitonin (0.47 ng/mL vs. 0.14 ng/mL, p<0.001), and C-reactive protein (203 mg/L vs. 128 ng/mL, p<0.001) were significantly higher in the non-survivors group. Also, baseline D-dimer levels were doubled in non-survivors group (1,025 ng/mL vs. 505 ng/mL, p=0.002) and specifically the differences were strengthened on day 3 (1,550 ng/mL vs. 540 ng/mL, p<0.001). Among organ-injury biochemical biomarkers, baseline glucose, CK, CK-MB, high-sensitive troponin T, and lactate dehydrogenase (LDH) were markedly higher in non-survivors (Table 2).
There were non-significant differences between groups in erythrocyte sedimentation rate, serum ferritin nor fibrinogen serum levels. However, in non-survivors group the absolute Lymphocyte Count was lower (0.9 vs. 1.1, p=0.021), as well as the lymphocyte-to-C-reactive protein ratio (4.4 vs. 9.0, p<0.001), total proteins, and serum albumin (2.9 g/dL vs. 3.5 g/dL, p<0.001) than in survivors group (Table 2).

4. Predictors of mortality

Several variables such as age, days from symptom onset to admission, dyspnea mMRC ≥3, respiratory rate, heart rate, SpO2/FiO2, qSOFA score, IMV, hematological and inflammation-related indices and organ-injury biochemical biomarkers were associated with mortality in the univariate logistic regression analysis. However, after multivariate logistic regression, dyspnea mMRC ≥3 (odds ratio [OR], 3.59; 95% confidence interval [CI], 1.51-8.52), SpO2/FiO2 ratio lower than 420 (OR, 6.44; 95% CI, 2.33-17.83), qSOFA score more than 2 points (OR, 3.46; 95% CI, 1.04-11.58) and need for IMV (OR, 64.7; 95% CI, 15.20-275.39) remained as independent risk factors for mortality. Some laboratory biomarkers as NLR ≥9 (OR, 4.64; 95% CI, 2.05-10.53), serum albumin <3.5 g/dL (OR, 3.76; 95% CI, 1.56-9.07), and LDH ≥725 U/L (OR, 5.45; 95% CI, 2.36-12.57) on admission, were independent risk factors associated with mortality in COVID-19 patients (Tables 3, 4).

Discussion

According to a new report by the Pan-American Health Organization (PAHO), the epicenter of the epidemic is currently in countries of America, with more cases reported daily and a higher mortality rate from COVID-19 [16]. As well as in Mexico, many countries in Latin America share a significant prevalence of chronic diseases, thus as emerging economies, there are a considerable number of socially vulnerable groups with economic difficulties in the effective fulfillment of home isolation. The latter, coupled with other socio-economic problems [17,18], converge on the alarming current state of this health emergency. Particularly in Mexico, since the opening of economic activity there has been an increase of 194% in the number of positive cases and mortality by COVID-19 has been tripled [11]. In the absence of specific treatment and an effective vaccine development in the short term, identification and recognition of mortality-related risk factors are considered vitally important for providing timely medical interventions and clinical decision-making on the patient care site.
Our results indicate, as has been consistently reported in previous studies, that age is a significant risk factor for severity and mortality in patients with COVID-19 [19,20]; a cutoff value of 50 years old has been significantly associated with mortality. Certainly, immunosenescence has been involved as an inadequate adaptive immune response leading to poor results [21]. We did not observe differences regarding gender in mortality as had been showed in other cohorts [19,20]. The prevalence of chronic diseases in our population did not allow us to differentiate if any might be considered as a risk factor for mortality since roughly eight out of 10 hospitalized patients have at least one comorbidity. Obesity is a public health problem, occupying the first place in prevalence among the Mexican population, responsible for severe forms of COVID-19 [22,23].
Regarding clinical characteristics of our population admitted with COVID-19, we identified that survivors had more signs and symptoms of the disease, although non-survivors showed a higher intensity in dyspnea measured by the mMRC scale. On physical examination, non-survivors have lower baseline SpO2 with a lower SpO2/FiO2 ratio. This ratio is a well-validated rapid detection index of acute respiratory distress syndrome (ARDS), which correlates with the PaO2/FiO2 calculation where blood gas testing may not be feasible [24]. Therefore, SpO2/FiO2 ratio should be used to early discriminate patients who benefit from aggressive therapy in order to improve oxygenation levels, as more the delay with oxygen support the higher organ damage [25]. A cutoff value of SpO2/FiO2 <420 is reasonable to closely monitor patients and take timely decision-making for admission to ICU’s, since it corresponds to 90% of saturated hemoglobin with a partial oxygen pressure of 60 mm Hg [24-26]. It should be noted that 43% of our population required IMV with an overall mortality rate of 82.5%. This numbers are related with previous reports in countries facing the worse scenario of COVID-19 (Wuhan and New York City) [27,28].
According to serum biomarkers univariate analysis revealed several abnormalities in the non-survivor’s group, some of them are described in previous published reports [29,30]. Nonetheless, in our population the independent variables associated with mortality were NLR, LDH, and serum albumin. The NLR is related with a moderate-to-severe ARDS (NLR >11) [31,32] but also, it is an independent risk factor for intrahospital mortality [32,33]. The cutoff level of NLR in our population was ≥9 and still independently related with mortality. LDH enzyme is a biomarker of severity in many systemic diseases and in patients with COVID-19, increased levels (>445 U/L) have been associated with organic damage and extensive lung injury [29,33]. However, our entire cohort of patients expressed higher values of LDH contrasting to previous reports (≥725 U/L) suggesting either a more severe disease expression or a delay in medical evaluation [33]. Lower levels of serum albumin is an indicator of abnormal liver function and according to the result of a recent meta-analysis, the lower the level of serum albumin the higher risk of a severe or fatal COVID-19 disease due to a delay in viral clearance [4,34].
In addition to the complexity of COVID-19 disease, many challenges are faced in Mexico, including a higher prevalence of chronic diseases, socio-economic factors, the economic reopening and according to WHO guidelines, insufficient hospital infrastructure by population [35], where ICUs represent a limited and invaluable resource for better outcomes in patients. Furthermore, low- and middle-income countries are at risk for an inability to manage an anticipated surge of critically ill COVID-19 patients, even though current literature recommend expanding critical care to guarantee quality of care for these patients. Current estimates suggesting the availability of 0.1 to 2.5 ICU beds per 100,000 population [36]. As well, several studies have shown that among hospitalized patients, 5% to 33% will require admission to an ICU and 75% to 100% of them will require support with IMV [1,27,37]. Mortality of critically ill patients with COVID-19 varies significantly among the published case series, ranging from 26% to 92% [27,28,38]. Supporting this data, in our cohort, we observed that mortality was higher in patients who did not receive a critical support treatment on an ICU. This wide variability can be explained by different case mixes, different organization or availability of ICU’s beds among different countries [28,36-38]. For that reason, COVID-19 represents a massive challenge for health care systems and the ICUs throughout the world and specially in Mexico for being among the low-and middle-income countries [36].
In conclusion, our findings could be usefully for decision-making to improve outcomes and prognosis of patients with COVID-19. Furthermore, may offer a national perspective since the information provided is relevant in order to priorate the needs, especially in low-and-middle income countries (Latin America), where the atmosphere of political disputes, the misinformation and lack of credibility in health authorities make it difficult to set priorities, allocate healthcare resources focusing on equipment and critical care, establish preventive strategies for disease transmission and prevent the collapse of the health system.
Our study has some limitations. First, this study was conducted at a single-center hospital with limited sample size. However, it is a designated center to treat COVID-19. Second, even though we were able to systematically register the information, some specific data might be missed. Third, we were not able to perform blood gas analysis in all patients.

Notes

Authors’ Contributions

Conceptualization: Cortés-Tellés A. Methodology: Cortés-Tellés A, López-Romero S, Mancilla-Ceballos R, Ortíz-Farías DL, Núñez-Caamal N, Figueroa-Hurtado E. Formal analysis: Cortés-Tellés A, López-Romero S, Mancilla-Ceballos R. Data curation: López-Romero S, Mancilla-Ceballos R. Investigation: Cortés-Tellés A, López-Romero S, Mancilla-Ceballos R, Ortíz-Farías DL, Núñez-Caamal N, Figueroa-Hurtado E. Writingoriginal draft preparation: Cortés-Tellés A, López-Romero S, Mancilla-Ceballos R, Ortíz-Farías DL, Núñez-Caamal N, Figueroa-Hurtado E. Writing-review and editing: Cortés-Tellés A, López-Romero S, Mancilla-Ceballos R, Ortíz-Farías DL, Núñez-Caamal N, Figueroa-Hurtado E. Approval of final manuscript: all authors.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Funding

No funding to declare.

Acknowledgments

To ICU staff: Gonzalo Renán Pantoja-Silveira, Irving SosaMarrufo, Yareth Maldonado, Sergio Bonfil, Erik Francisco Romero-Mejía, Freddy Avila, Gerardo Díaz. Internal Medicine staff: Jorge Arturo Valdivieso-Jiménez, Genaro Suárez, Roberto Ambriz Huerta, José Manuel Burgos Borges, Juan Erick Aceves-Díaz. Jesús Antonio Tut-Bojorquez. ICU and general ward nursing staff. X-ray technicians, Respiratory therapists and Nutritionist staff. Dra. Mussaret Zaidi Jacobson for her support in reviewing the manuscript.

Table 1.
Demographic and clinical characteristics of patients with COVID-19
Variable Total (n=200) Non-survivor (n=77, 38%) Survivor (n=123, 62%) p-value
Age, yr 55 (41-65) 60 (51-70) 50 (38-60) <0.001
 18-40 43 (22) 4 (5) 39 (32) <0.001
 41-64 101 (51) 39 (51) 62 (51) 0.973
 >65 55 (28) 33 (43) 22 (18) <0.001
Male sex 138 (69) 55 (71) 83 (67) 0.557
Comorbidities 144 (72) 54 (70) 90 (73) 0.641
 Diabetes 55 (28) 24 (31) 31 (25) 0.358
 Hypertension 61 (30) 25 (32) 36 (29) 0.633
 Obesity 103 (52) 40 (53) 63 (52) 0.892
 COPD 6 (3) 3 (4) 3 (2) 0.557
 Current smoker 14 (7) 4 (5) 10 (8) 0.429
 Cardiovascular disease 4 (2) 2 (3) 2 (2) 0.633
 HIV 4 (2) 2 (3) 2 (2) 0.633
 Other 17 (8) 13 (17) 4 (3) 0.001
Days from symptom onset to admission 8 (6-9) 7 (5-9) 8 (6-10) 0.012
Symptoms on admission
 Fever 191 (96) 75 (97) 116 (94) 0.304
 Maximum temperature registrated, °C 38.1 (38-38.5) 38.3 (38-38.5) 38 (38-38.5) 0.058
 Chills 44 (22) 25 (32) 19 (15) 0.005
 Cough 181 (90) 72 (94) 109 (88) 0.251
 Dyspnea 187 (93) 70 (91) 117 (95) 0.240
 Intensity (mMRC) 3 (2-3) 3 (3-3) 2 (2-3) <0.001
 Chest pain 85 (43) 25 (32) 60 (49) 0.023
 Headache 111 (56) 36 (47) 75 (61) 0.049
 Anosmia 25 (13) 3 (4) 22 (18) 0.004
 Ageusia 22 (11) 4 (5) 18 (15) 0.038
 Myalgia/arthralgia 109 (55) 34 (46) 75 (61) 0.020
Upon arrival
 Heart rate, BPM 100 (89-114) 107 (92-116) 98 (89-110) 0.022
 Respiratory rate, bpm 29 (24-32) 30 (25-35) 28 (24-31) 0.003
 Systolic blood pressure, mm Hg 120 (110-130) 120 (110-130) 120 (110-130) 0.759
 Dyastolic blood pressure, mm Hg 70 (70-80) 70 (66-80) 70 (70-80) 0.356
 SpO2, % 87 (78-90) 73 (60-85) 89 (86-92) <0.001
 SpO2/FiO2 414 (371-431) 348 (286-405) 424 (409-438) <0.001
 qSOFA score 1 (1-1) 1 (1-2) 1 (1-1) <0.001
Invasive mechanical ventilation 86 (43) 71 (82.5) 15 (17.5) <0.001
 ICU admission 44 (51) 32 (72.7) 12 (27.3) 0.014
 Non-ICU admission 42 (49) 39 (92.8) 3 (7.2) 0.014
Hospital stay, day 6 (4-12) 8 (4-16) 6 (4-9) 0.057

Values are presented as median (IQR) or number (%).

p-values by the rank-sum Wilcoxon test, chi-squared test, or Fisher exact test, as appropriate.

COVID-19: coronavirus disease pandemic 2019; COPD: chronic obstructive pulmonary disease; HIV: human immunodeficiency virus; mMRC: modified Medical Research Council; BPM: beats per minute; bpm: breaths per minute; SpO2: pulse oximetric saturation; SpO2/FiO2: pulse oximetric saturation/fraction of inspired oxygen; qSOFA: quick Sequential Organ Failure Assessment; ICU: intensive care unit; IQR: interquartile range.

Table 2.
Laboratory value differences between non-survivors and survivors
Variable Total (n=200) Non-survivor (n=77, 38%) Survivor (n=123, 62%) p-value
Hematological and inflammation-related indices
 White-cell count, ×103/µL 10.6 (7.8-14.4) 13.1 (10.5-16.9) 9.4 (7.2-12.4) <0.001
 Neutrophils, ×103/µL 8.7 (5.6-12.4) 11.2 (8.6-14.6) 7.2 (4.9-10.7) <0.001
 Lymphocyte count, ×103/µL 1.0 (0.7-1.6) 0.9 (0.7-1.4) 1.1 (0.8-1.6) 0.021
 Lymphopenia <1,000 cells×109 92 (46) 46 (59) 46 (37) 0.002
 Neutrophil-to-lymphocyte ratio 7.8 (4.3-14.1) 12.3 (7.7-18.3) 6.3 (3.8-10.8) <0.001
 Procalcitonin (ng/mL) 0.22 (0.11-0.63) 0.47 (0.18-2.40) 0.14 (0.07-0.28) <0.001
 CRP, mg/L 149 (74-259) 203 (96-323) 128 (63-225) <0.001
 Lymphocyte-to-CRP ratio 7.1 (3.5-18.5) 4.4 (2.5-11.5) 9.0 (4.1-21.7) <0.001
 ESR, mm/hr 22 (15-31) 26 (15-35) 20 (15-28) 0.148
 Serum ferritin, ng/L 1,278 (652-1,962) 1,460 (691-2,109) 1,151 (642-1,923) 0.246
Coagulation factors
 D-dimer, ng/mL 610 (330-1,780) 1,025 (470-3,060) 505 (300-1,440) 0.002
 D-dimer on day 3, ng/mL 903 (426-2,280) 1,550 (870-3,960) 540 (280-1,470) <0.001
 Fibrinogen, mg/dL 683 (459-660) 701 (586-866) 674 (518-826) 0.216
Organ-injury biochemical biomarkers
 Serum glucose, mg/dL 133 (100-181) 149 (107-195) 120 (96-176) 0.018
 CK, U/L 98 (56-216) 126 (60-278) 90 (45-154) 0.033
 Total proteins, g/dL 6.5 (6.1-7.1) 6.2 (5.7-6.9) 6.8 (6.4-7.3) <0.001
 Serum albumin, g/dL 3.2 (2.8-3.6) 2.9 (2.6-3.3) 3.5 (3.1-4.0) <0.001
 CK-MB, U/L 23 (18-36) 31 (22-39) 21 (17-29) <0.001
 Troponin T, pg/mL 8 (3-27) 27 (8-55) 5 (3-11) <0.001
 LDH, U/L 722 (530-991) 934 (722-1,201) 628 (511-798) <0.001

Values are presented as median (IQR) or number (%).

p-values by the rank-sum Wilcoxon test, chi-squared test, or Fisher exact test, as appropriate.

CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; CK: creatine phosphokinase; CK-MB: creatine phosphokinase MB fraction; LDH: lactate dehydrogenase; IQR: interquartile range.

Table 3.
Clinical risk factors associated with mortality
Variable Univariate analysis
Multivariate analysis
OR 95% CI p-value OR 95% CI p-value
Age, yr 1.05 1.03-1.08 <0.001 - - -
 18-40 0.12 0.04-0.35 <0.001 0.06 0.01-0.54 0.012
 >65 3.44 1.81-6.56 <0.001 - - -
 ≥55 vs. <55 3.59 1.94-6.61 <0.001 - - -
Days from symptom onset to admission (<7 vs. ≥7) 1.16 1.05-1.28 0.004 - - -
Dyspnea mMRC (≥3 vs. <3) 5.73 2.67-12.32 <0.001 3.59 1.51-8.52 0.004
Chest pain (yes vs. no) 0.47 0.26-0.87 0.016 - - -
Anosmia (yes vs. no) 0.13 0.03-0.56 0.006 - - -
Ageusia (yes vs. no) 0.25 0.07-0.87 0.029 - - -
Myalgias/artralgia (yes vs. no) 0.52 0.29-0.92 0.026 - - -
Respiratory rate (≥30 bpm vs. <30 bpm) 4.95 1.38-17.75 0.014 - - -
Heart rate (≥100 BPM vs. <100 BPM) 1.82 0.99-3.32 0.052 - - -
SpO2/FiO2 (<420 vs. ≥420) 7.32 3.52-15.22 <0.001 6.44 2.33-17.83 0.003
qSOFA score (≥2 points vs. <2 points) 6.08 2.54-14.56 <0.001 3.46 1.04-11.58 0.044
Invasive mechanical ventilation (yes vs. no) 81.60 30.19-220.52 <0.001 64.7 15.20-275.39 <0.001

OR: odds ratio; CI: confidence interval; mMRC: modified Medical Research Council; bpm: breaths per minute; BPM: beats per minute; SpO2/FiO2: pulse oximetric saturation/fraction of inspired oxygen; qSOFA: quick Sequential Organ Failure Assessment.

Table 4.
Laboratory value risk factors associated with mortality
Univariate analysis
Multivariate analysis
OR 95% CI p-value OR 95% CI p-value
Hematological and inflammation-related indices
 White-cell count (≥12×103/µL vs. <12×103/µL) 4.23 2.29-7.84 <0.001 - - -
 Neutrophil count (≥10×103/µL vs. <10×103/µL) 4.68 2.51-8.71 <0.001 - - -
 Neutrophil-to-lymphocyte ratio (≥9 vs. <9) 5.69 3.01-10.76 <0.001 4.64 2.05-10.53 <0.001
 Procalcitonin (≥0.5 ng/mL vs. <0.5 ng/mL) 6.16 3.13-12.09 <0.001 - - -
 CRP (≥150 mg/L vs. <150 mg/L) 1.85 1.03-3.35 0.041 - - -
 Lymphocyte-to-CRP (<6 vs. ≥6) 0.97 0.95-0.99 0.013 - - -
Coagulation factors
 D-dimer baseline (≥700 ng/mL vs. <700 ng/mL) 2.64 1.44-4.84 0.002 - - -
 D-dimer day 3 (≥1,000 ng/mL vs. <1,000 ng/mL) 4.69 2.28-9.65 <0.001 - - -
Organ-injury biochemical biomarkers
 Glucose (≥130 mg/dL vs. <130 mg/dL) 2.58 1.42-4.72 0.002 - - -
 Protein level (<6.5 g/dL vs. ≥6.5 g/dL) 5.04 2.43-10.47 <0.001 - - -
 Serum albumin (<3.5g/dL vs. ≥3.5 g/dL) 4.62 2.14-9.95 <0.001 3.76 1.56-9.07 0.003
 Troponin T (≥10 pg/mL vs. <10 pg/mL) 6.30 3.30-12.05 <0.001 - - -
 CK-MB (≥25 U/L vs. <25 U/L) 3.17 1.72-5.83 <0.001 - - -
 LDH (≥725 U/L vs. <725 U/L) 5.85 3.03-11.27 0.004 5.45 2.36-12.57 <0.001

OR: odds ratio; CI: confidence interval; CRP: C-reactive protein; CK-MB: creatine kinase mb fraction; LDH: lactate dehydrogenase.

References

1. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA 2020;323:2052-9.
crossref pmid pmc
2. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med 2020;180:934-43.
crossref pmid
3. Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA 2020;323:1574-81.
crossref pmid pmc
4. Henry BM, de Oliveira MH, Benoit S, Plebani M, Lippi G. 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;58:1021-8.
crossref pmid
5. Johns Hopkins University and Medicine; Coronavirus Resource Center. Mortality analyses: mortality in the most affected conuntries [Internet]. Baltimore: Johns Hopkins University and Medicine; 2020 [cited 2020 Jul 5]. Available from: https://coronavirus.jhu.edu/data/mortality.

6. World Health Organization. WHO coronavirus disease (COVID-19) dashboard [Internet]. Geneva: World Health Organization; 2020 [cited 2020 Jul 13]. Available from: https://covid19.who.int/.

7. Rivera Donmarco JA, Colchero MA, Fuentes ML, de Cosio Martinez TG, Salinas CA, Licona GH, et al. Postura. Recommendations for a State policy for the prevention and control of obesity in Mexico in the 2018-2024 period. Obesity in Mexico. State of public policy and recommendations for its prevention and control. Cuernavaca: Instituto Nacional de Salud Pública; 2018. p. 15-30.

8. Campos-Nonato I, Hernadez-Barrera L, Pedroza-Tobias A, Medina C, Barquera S. Hypertension in Mexican adults: prevalence, diagnosis and type of treatment. Ensanut MC 2016. Salud Publica Mex 2018;60:233-43.
crossref pmid
9. Basto-Abreu A, Barrientos-Gutierrez T, Rojas-Martinez R, Aguilar-Salinas CA, Lopez-Olmedo N, De la Cruz-Gongora V, et al. Prevalence of diabetes and poor glycemic control in Mexico: results from Ensanut 2016. Salud Publica Mex 2020;62:50-9.
crossref pmid
10. Chen R, Liang W, Jiang M, Guan W, Zhan C, Wang T, et al. Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China. Chest 2020;158:97-105.
crossref pmid pmc
11. Mahase E. Covid-19: deaths in Mexico triple since reopening began in June. BMJ 2020;370:m2753.
crossref pmid
12. World Health Organization. Global surveillance for human infection with coronavirus disease (COVID-19): interim guidance 2020 [Internet]. Geneva: World Health Organization; 2020 [cited 2020 Apr 20]. Available from: https://www.who.int/publications/i/item/who-2019-nCoV-surveillanceguidance-2020.7.

13. Riis P. Thirty years of bioethics: the Helsinki Declaration 1964-2003. New Rev Bioeth 2003;1:15-25.
crossref pmid
14. Dirección General de Epidemiología. Biosafety and biosecurity protocol for patient management during sample collection. Of suspected disease cases by 2019-nCov 2020 [Internet]. Mexico City: Gobierno de Mexico; 2020 [cited 2020 Apr 15]. Available from: https://www.gob.mx/cms/uploads/attachment/file/531376/Protocolo_de_Bioseguridad_y_Biocustodia_2019-nCOV__Caso_sospechosos_InDRE_31012020.pdf.

15. World Health Organization. Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected. Interim guidance 2020, March 13 [Internet]. Geneva: World Health Organization; 2020 [cited 2020 Apr 19]. Available from: https://www.who.int/docs/default-source/coronaviruse/clinical-management-of-novel-cov.pdf.

16. Organización Panamericana de la Salud. Nuevo informe detalla la amplia respuesta de a OPS a la pandemia por COVID-19 en las Américas - OPS/OMS [Internet]. Washington, DC: Pan American Health Organization; [cited 2020 Jul 9]. Available from: https://www.paho.org/es/noticias/2-7-2020-nuevoinforme-detalla-amplia-respuesta-ops-pandemia-por-covid19-americas.

17. Vwlimayo CR. The COVID-19 crisis in emerging and developing countries [Internet]. Mexico City: Nexos; 2020 [cited 2020 Jul 9]. Avaialable from: https://economia.nexos.com.mx/?p=3044.

18. United Nations Development Program. Diagnosis of the socioeconomic situation in Mexico. Development challenges before COVID-19 in Mexico. Socio-economic panorama. Mexico City: United Nations Development Program; 2020.

19. Ji D, Zhang D, Xu J, Chen Z, Yang T, Zhao P, et al. Prediction for progression risk in patients with COVID-19 pneumonia: the CALL score. Clin Infect Dis 2020;71:1393-9.
crossref pmid
20. Du RH, Liang LR, Yang CQ, Wang W, Cao TZ, Li M, et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J 2020;55:2000524.
crossref pmid pmc
21. Opal SM, Girard TD, Ely EW. The immunopathogenesis of sepsis in elderly patients. Clin Infect Dis 2005;41 Suppl 7:S504-12.
crossref pmid
22. Samuels JD. Obesity phenotype is a predictor of COVID-19 disease susceptibility. Obesity (Silver Spring) 2020;28:1368.
crossref pmid
23. Caussy C, Wallet F, Laville M, Disse E. Obesity is associated with severe forms of COVID-19. Obesity (Silver Spring) 2020;28:1175.
crossref pmid
24. Rice TW, Wheeler AP, Bernard GR, Hayden DL, Schoenfeld DA, Ware LB, et al. Comparison of the SpO2/FIO2 ratio and the PaO2/FIO2 ratio in patients with acute lung injury or ARDS. Chest 2007;132:410-7.
crossref pmid
25. Lu X, Jiang L, Chen T, Wang Y, Zhang B, Hong Y, et al. Continuously available ratio of SpO2/FiO2 serves as a noninvasive prognostic marker for intensive care patients with COVID-19. Respir Res 2020;21:194.
crossref pmid pmc
26. Schwartzstein RM, Parker MJ. Respiratory physiology: a clinical approach (integrated physiology). Baltimore: Lippincott Williams & Wilkins; 2006.

27. Hua J, Qian C, Luo Z, Li Q, Wang F. Invasive mechanical ventilation in COVID-19 patient management: the experience with 469 patients in Wuhan. Crit Care 2020;24:348.
crossref pmid pmc
28. Gupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML, et al. Factors associated with death in critically ill patients with coronavirus disease 2019 in the US. JAMA Intern Med 2020;180:1436-46.
crossref pmid
29. Sun S, Cai X, Wang H, He G, Lin Y, Lu B, et al. Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China. Clin Chim Acta 2020;507:174-80.
crossref pmid pmc
30. Chen R, Sang L, Jiang M, Yang Z, Jia N, Fu W, et al. Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China. J Allergy Clin Immunol 2020;146:89-100.
crossref pmid pmc
31. Ma A, Cheng J, Yang J, Dong M, Liao X, Kang Y. Neutrophil-to-lymphocyte ratio as a predictive biomarker for moderate-severe ARDS in severe COVID-19 patients. Crit Care 2020;24:288.
crossref pmid pmc
32. Liu Y, Du X, Chen J, Jin Y, Peng L, Wang HH, et al. Neutrophilto-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. J Infect 2020;81:e6-12.
crossref
33. Li X, Xu S, Yu M, Wang K, Tao Y, Zhou Y, et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J Allergy Clin Immunol 2020;146:110-8.
crossref pmid pmc
34. Fu Y, Han P, Zhu R, Bai T, Yi J, Zhao X, et al. Risk factors for viral RNA shedding in COVID-19 patients. Eur Respir J 2020;56:2001190.
crossref pmid pmc
35. United Nations Development Program. Development challenges in the face of COVID-19 in Mexico: overview from a health perspective. Mexico City: United Nations Development Program; 2020.

36. Turner HC, Hao NV, Yacoub S, Hoang VMT, Clifton DA, Thwaites GE, et al. Achieving affordable critical care in low-income and middle-income countries. BMJ Glob Health 2019;4:e001675.
crossref pmid pmc
37. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020;382:1708-20.
crossref pmid
38. Grasselli G, Greco M, Zanella A, Albano G, Antonelli M, Bellani G, et al. Risk factors associated with mortality among patients with COVID-19 in intensive care units in Lombardy, Italy. JAMA Intern Med 2020;180:1345-55.
crossref pmid


ABOUT
ARTICLE & TOPICS
Article category

Browse all articles >

Topics

Browse all articles >

BROWSE ARTICLES
FOR CONTRIBUTORS
Editorial Office
101-605, 58, Banpo-daero, Seocho-gu (Seocho-dong, Seocho Art-Xi), Seoul 06652, Korea
Tel: +82-2-575-3825, +82-2-576-5347    Fax: +82-2-572-6683    E-mail: katrdsubmit@lungkorea.org                

Copyright © 2021 by The Korean Academy of Tuberculosis and Respiratory Diseases. All rights reserved.

Developed in M2PI

Close layer
prev next