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Khan\" }, { \"@type\": \"Person\", \"name\": \"Mohammad Asim\" }, { \"@type\": \"Person\", \"name\": \"Hassan Al-Thani\" }, { \"@type\": \"Person\", \"name\": \"Mohammed Abukhattab\" }, { \"@type\": \"Person\", \"name\": \"Muna Al Maslamani\" } ], \"publisher\": { \"@type\": \"Organization\", \"name\": \"F1000Research\", \"logo\": { \"@type\": \"ImageObject\", \"url\": \"https://f1000research.com/img/AMP/F1000Research_image.png\", \"height\": 480, \"width\": 60 } }, \"image\": { \"@type\": \"ImageObject\", \"url\": \"https://f1000research.com/img/AMP/F1000Research_image.png\", \"height\": 1200, \"width\": 150 }, \"description\": \" Background This study investigated the utility of platelet-to-lymphocyte ratio (PLR) and Neutrophil-to-Lymphocyte ratio (NLR) in patients with COVID-19 with respect to age, early (a week) vs. delayed recovery (&gt; a week) and mortality. Methods This was a retrospective study including 1,016 COVID-19 patients. The discriminatory power and multivariate logistic regression analysis were performed. Results The mean age of patients was 45 (± 13.9), and 75.7% were males. Older patients had elevated NLR, PLR, D-dimer, CRP, and Interleukin-6 levels and longer hospital stay than the younger group (p &lt; 0.001). In-hospital mortality was higher in older adults (26.9% vs. 6.6%, p =0.001). On-admission NLR (5.8 vs. 3.2; P= 0.001) and PLR (253.9±221.1 vs. 192.2±158.5; p = 0.004) were higher in the non-survivors than survivors. Both PLR and NLR displayed significant discriminatory ability for mortality. NLR had a higher AUC and specificity, while PLR exhibited slightly higher sensitivity. In individuals aged ≤55, NLR showed superior discrimination (AUC=0.717) compared to PLR (AUC=0.620). Conversely, for older adults, PLR displayed enhanced discrimination (AUC=0.710), while NLR showed AUC=0.693. Conclusion Higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. \" } { \"@context\": \"http://schema.org\", \"@type\": \"BreadcrumbList\", \"itemListElement\": [ { \"@type\": \"ListItem\", \"position\": \"1\", \"item\": { \"@id\": \"https://f1000research.com/\", \"name\": \"Home\" } }, { \"@type\": \"ListItem\", \"position\": \"2\", \"item\": { \"@id\": \"https://f1000research.com/browse/articles\", \"name\": \"Browse\" } }, { \"@type\": \"ListItem\", \"position\": \"3\", \"item\": { \"@id\": \"https://f1000research.com/articles/13-446/v2\", \"name\": \"Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting...\" } } ] } Home Browse Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article El-Menyar A, Khan NA, Asim M et al. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.12688/f1000research.146814.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] Ayman El-Menyar https://orcid.org/0000-0003-2584-953X 1,2 , Naushad A. Khan 1 , Mohammad Asim 1 , Hassan Al-Thani 3 , Mohammed Abukhattab 4 , Muna Al Maslamani 4 Ayman El-Menyar https://orcid.org/0000-0003-2584-953X 1,2 , Naushad A. Khan 1 , [...] Mohammad Asim 1 , Hassan Al-Thani 3 , Mohammed Abukhattab 4 , Muna Al Maslamani 4 PUBLISHED 07 Jul 2025 Author details Author details 1 Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Doha, Qatar 2 Clinical Medicine, Weill Cornell Medical College, Doha, Qatar 3 Department of Surgery, Hamad Medical Corporation, Doha, Doha, Qatar 4 Department of Medicine, Hamad Medical Corporation, Doha, Doha, Qatar Ayman El-Menyar Roles: Conceptualization, Methodology, Writing – Original Draft Preparation Naushad A. Khan Roles: Data Curation, Formal Analysis, Writing – Review & Editing Mohammad Asim Roles: Data Curation, Formal Analysis, Methodology Hassan Al-Thani Roles: Methodology, Writing – Review & Editing Mohammed Abukhattab Roles: Conceptualization, Methodology Muna Al Maslamani Roles: Conceptualization, Methodology OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Coronavirus (COVID-19) collection. Abstract Background This study investigated the utility of platelet-to-lymphocyte ratio (PLR) and Neutrophil-to-Lymphocyte ratio (NLR) in patients with COVID-19 with respect to age, early (a week) vs. delayed recovery (> a week) and mortality. Methods This was a retrospective study including 1,016 COVID-19 patients. The discriminatory power and multivariate logistic regression analysis were performed. Results The mean age of patients was 45 (± 13.9), and 75.7% were males. Older patients had elevated NLR, PLR, D-dimer, CRP, and Interleukin-6 levels and longer hospital stay than the younger group (p < 0.001). In-hospital mortality was higher in older adults (26.9% vs. 6.6%, p =0.001). On-admission NLR (5.8 vs. 3.2; P = 0.001) and PLR (253.9±221.1 vs. 192.2±158.5; p = 0.004) were higher in the non-survivors than survivors. Both PLR and NLR displayed significant discriminatory ability for mortality. NLR had a higher AUC and specificity, while PLR exhibited slightly higher sensitivity. In individuals aged ≤55, NLR showed superior discrimination (AUC=0.717) compared to PLR (AUC=0.620). Conversely, for older adults, PLR displayed enhanced discrimination (AUC=0.710), while NLR showed AUC=0.693. Conclusion Higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. READ ALL READ LESS Keywords COVID-19; Inflammation; Mortality; Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, Hospital stay, age Corresponding Author(s) Ayman El-Menyar ( [email protected] ) Close Corresponding author: Ayman El-Menyar Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 El-Menyar A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: El-Menyar A, Khan NA, Asim M et al. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.12688/f1000research.146814.2 ) First published: 03 May 2024, 13 :446 ( https://doi.org/10.12688/f1000research.146814.1 ) Latest published: 07 Jul 2025, 13 :446 ( https://doi.org/10.12688/f1000research.146814.2 ) Revised Amendments from Version 1 The revised manuscript confirms the association between NLR and PLR and hospitalization duration and recovery, taking into account the patient's age. The findings, interestingly, showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. The current study provides unique insights into biomarker behavior within a younger, predominantly male cohort, a demographic that has been underrepresented in earlier studies, which have focused mainly on elderly or critically ill populations. Stratified analysis revealed that NLR exhibited greater discriminatory ability for mortality in patients aged ≤55 years, whereas PLR performed better in those aged >55 years. These age-dependent differences in performance, which are not well documented in the previous literature, suggest a need for a tailored interpretation of these markers across age groups. Moreover, in contrast to most existing studies that focus solely on mortality or ICU admission, we also evaluated the association between NLR/PLR and hospital length of stay. The mean duration of hospital stay was 21.9 ± 17.0 days, with a median stay of 19.6 days. The study cohort comprised patients from 43 different nationalities, reflecting the ethnically diverse population of Qatar. Ethnicity-wise, most patients were of South Asian origin, followed by those of Arab ethnicity. Other represented groups included Southeast Asians, East Africans, and Central Asians. The revised manuscript confirms the association between NLR and PLR and hospitalization duration and recovery, taking into account the patient's age. The findings, interestingly, showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. The current study provides unique insights into biomarker behavior within a younger, predominantly male cohort, a demographic that has been underrepresented in earlier studies, which have focused mainly on elderly or critically ill populations. Stratified analysis revealed that NLR exhibited greater discriminatory ability for mortality in patients aged ≤55 years, whereas PLR performed better in those aged >55 years. These age-dependent differences in performance, which are not well documented in the previous literature, suggest a need for a tailored interpretation of these markers across age groups. Moreover, in contrast to most existing studies that focus solely on mortality or ICU admission, we also evaluated the association between NLR/PLR and hospital length of stay. The mean duration of hospital stay was 21.9 ± 17.0 days, with a median stay of 19.6 days. The study cohort comprised patients from 43 different nationalities, reflecting the ethnically diverse population of Qatar. Ethnicity-wise, most patients were of South Asian origin, followed by those of Arab ethnicity. Other represented groups included Southeast Asians, East Africans, and Central Asians. See the authors' detailed response to the review by Lorenzo Malatino and Ivan Isaia See the authors' detailed response to the review by Roberto Paganelli READ REVIEWER RESPONSES Key messages - Simple, instant bedside laboratory tests on admission are of utmost value for patients’ stratification during a pandemic. - COVID-19 patients with elevated NLR and PLR levels are associated with delayed recovery, more ICU admissions, and intubation. - A greater NLR values are associated with higher mortality in older COVID-19 patients. - However, none of these two parameters alone is an independent predictor of death. - These findings highlight the potential utility of NLR and PLR as accessible, cost-effective tools for early risk stratification, particularly in resource-limited or surge settings. Introduction The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) causing Coronavirus disease 2019 (COVID-19) has overwhelmed the healthcare infrastructure worldwide by causing recurrent waves. 1 The SARS-CoV-2 infection has wide clinical variations, ranging from asymptomatic infection to moderate upper respiratory tract illness to severe viral pneumonia with respiratory failure and death. 2 Of note, reliable laboratory parameters of the severity of the disease, treatment response, and outcome were not thoroughly investigated during the early phase of the pandemic due to its rapid onset and spread. Consequently, the early identification of clinical and laboratory variables linked with poor outcomes is critical for identifying low- and high-risk patients for triage and guiding appropriate management. Infectious diseases are associated with inflammation, and existing data supports the central role of inflammation in the progression and pathogenesis of COVID-19. 2 SARS-COV-2 viral replication causes cellular destruction, leading to the release of cytokines and chemokines from the activated macrophages. 3 As a result, these mediators set off immunological responses, which in turn cause cytokine storms and aggravate the disease. As a result, they elicit immune responses, which create cytokine storms and exacerbate the problem. This imbalance arises because the adaptive immune response depends on the strength of the inflammatory response. 4 Consequently, patients with a pre-existing chronic inflammatory status may be more vulnerable to a severe form of COVID-19 disease. The Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are straightforwardly obtainable ratios from complete blood count (CBC) panels. Emerging evidence suggests that peripheral NLR and PLR can be used as markers of systemic inflammation in various disease processes. 5 – 9 Several studies have reported the prognostic role of NLR in differentiating mild/moderate cases from severe COVID-19 cases and have proposed that NLR can be a reliable predictor of COVID-19 progression associated with high mortality in COVID-19. 10 – 16 Moreover, several studies have also suggested PLR to be a promising and reliable indicator of disease severity, exhibiting good predictive values on progression and clinical outcomes in patients with COVID-19. 17 – 22 However, a limiting factor of these ratios is the inability to account for ethnic differences. 23 Also, these ratios can be profoundly influenced by age and gender, 24 , 25 though the extent of this influence has not yet been fully explored in COVID-19. Moreover, Qatar has a distinct demographic profile, with around 88% of the expatriate workforce of Qatar’s 2.8 million citizens. While the bulk of the population (75%) is male gender, the pyramid shape of population distribution is disproportionately concentrated in the 20–50-year age group. 25 COVID-19 affects males disproportionately, and older adults tend to have worse outcomes. 26 , 27 We sought to evaluate the association of NLR and PLR and the recovery and mortality in COVID-19 patients, and to assess age-stratified differences in these outcomes. Methods Study population and data collection A retrospective observational study was conducted, including patients with COVID-19 admitted to the different affiliated Hospitals of Hamad Medical Corporation (HMC) in Qatar at the beginning of the coronavirus pandemic (from March 01 to June 01, 2020). The subjects included in the study were laboratory-confirmed cases of COVID-19 disease (>18 years old) of both genders. Patients with an inconclusive diagnosis of COVID-19 by RT-PCR testing, undefined diagnosis, and missing data were excluded from the study. Data were extracted from the electronic medical record (CERNER), which included patients’ demographics such as (age, gender, nationality); recent exposure history, clinical symptoms and signs, comorbidities (Hypertension, diabetes mellitus, cancer, renal failure, chronic obstructive pulmonary disease, and others), initial vitals (Systolic blood pressure, diastolic blood pressure, pulse, respiratory rate, blood oxygen saturation), routine laboratory findings (initial and repeated readings) including CBC, blood chemistry and C-reactive protein (CRP), chest X-ray and computed tomographic scans, treatment, mechanical ventilation, hospital and intensive critical care (ICU) length of stay, speed of recovery (within one week, and more than one week), discharge from hospital and mortality. TaqPath COVID-19 Combo Kit TM (Thermo Fisher Scientific, Waltham, Massachusetts, USA) or Cobas SARS-CoV-2 Test ® (Roche Diagnostics, Rotkreuz, Switzerland) were used to identify SARS-CoV-2 infection utilizing Nasopharyngeal and throat samples. All COVID-19 testing was performed at the central laboratory of the HMC, which manages over 85% of the country’s inpatient bed capacity and is responsible for delivering public healthcare. Study definitions - Every patient who experienced COVID-19-like manifestations and at the same time tested positive for COVID-19 in respiratory samples using a real-time reverse-transcription polymerase chain reaction (RT-PCR) assay was deemed a confirmed COVID-19 case. - The platelet-to-lymphocyte ratio (PLR) was defined as the ratio between absolute Platelet counts to absolute lymphocyte count, and the neutrophil-to-lymphocyte ratio (NLR) was defined as the ratio between absolute neutrophil counts to absolute lymphocyte count. - Recovery referred to two negative swab tests done consecutively. Statistical analysis The data was collated in Microsoft Excel, and statistical analysis was performed using SPSS, version 28.0. for Windows (Armonk, NY: IBM Corp, USA). Data were expressed as proportions, means ± standard deviations, or medians as appropriate for continuous variables or as absolute counts and percentages for categorical variables. Data were compared using the student-t-test for continuous variables and the Pearson χ 2 test for categorical variables. The Fisher exact test was used if the expected cell frequency was below five. For skewed continuous data, a nonparametric Mann-Whitney test was performed. The independent predictors of mortality were identified using multivariable logistic regression analysis after adjusting for age, gender, comorbidities, complications, NLR, and PLR as covariates of interest. Areas under the curve (AUC) of ROC curves were employed to determine the ratios’ performance in age discrimination regarding NLR and PLR. The best cut-off points of the ratios were the points on the curves with the highest sensitivity and specificity. The sample size for the current study was not determined a priori as we intended to include all the laboratory-confirmed COVID-19 cases during the study period. A two-sided P -value < 0.05 was considered statistically significant. This observational study was conducted in accordance with the STROBE principles. The study was authorized by the Institutional Review Board and Medical Research Council (MRC-01-20-672 & MRC-05-213) of Hamad Medical Corporation. A waiver of consent was granted for this retrospective study as there was no direct contact was made with the participants, and the data was collected anonymously. Results During the study period, 1016 persons tested positive for SARS-CoV-2. The mean age of the cohort was 45±13.9 years, and an overwhelming majority of infected persons were male (75.7 %). The most common chronic medical conditions were hypertension (40.3%), followed by diabetes mellitus (39.0%), chronic kidney disease (14.0%), cancer (5.4%) and chronic obstructive pulmonary disease (4.8%). The mean duration of hospital stay was 21.9 ± 17.0 days, with a median stay of 19.6 days (range: 0.1–153 days). The study cohort consisted of patients representing 43 different nationalities, reflecting the ethnically diverse population of Qatar. Ethnicity wise, the majority of patients were of South Asian origin (55.0%), followed by those of Arab ethnicity (29.6%). Other represented groups included Southeast Asians (8.2%), East Africans (2.7%), and Central Asians (2.5%). Smaller proportions of patients were from European (0.9%), Latin American (0.8%), Central African, West African, and North American, backgrounds (each 0.1%). Table 1 outlines the comparison of clinical characteristics, in-hospital complications, comorbidities, and outcomes of COVID-19 patients according to hospital length of stay. Patients in the long-stay group were older (45.9±13.9 vs. 40.6±13.2), had significantly lower SpO 2 (97.1±3.6 vs. 98.4±2.0), and were more likely to have significant medical comorbidities compared to ‘short stay’ group. Compared with the short-stay group, patients in the long-stay group were presented with lower lymphocyte and platelet counts and higher inflammation-related indices (CRP, IL-6). Further significant elevations in NLR [3.7 (0.3-72.0) vs. 2.8 (0.6(0.6-53.0)); P =0.002] and the PLR indices was found [205.4±178.8 vs. 199.6±168.2; P =0.001]. Concerning the major in-hospital complications, patients in the long-hospital stay group were more likely to have renal failure (16.7% vs. 5.1%; P =0.001) and ARDS (3.7% vs. 0.0%; P =0.009) than patients in the short-stay group. The in-hospital mortality rate was 11.9% (121/1016). Patients in the long-stay group had higher in-hospital mortality than those in the short-stay group (12.8% vs. 7.9%; P <0.06). Table 1. Comparisons of clinical characteristics, and outcomes of COVID-19 patients according to hospital length of stay. Variables Length of hospital stays P -value Short stay (≤ 1 week) (n =178, 17.5%) Long stay (> 1 week) (n = 838, 82.5%) Age (years) 40.6±13.2 45.9±13.9 0.001 Males 105 (59.0%) 664 (79.2%) 0.001 Number of admissions 2 (1-14) 1 (1-57) 0.001 Initial vital signs Systolic blood pressure 125.9±20.3 127.1±18.6 0.45 Diastolic blood pressure 75.6±11.5 76.8±11.8 0.21 Pulse 90.3±15.1 90.8±15.7 0.73 Respiratory rate 19.3±2.5 20.8±5.6 0.001 Oxygen saturation 98.4±2.0 97.1±3.6 0.001 Comorbidities Hypertension 50 (28.1%) 359 (42.8%) 0.001 Diabetes Mellitus 50 (28.1%) 346 (41.3%) 0.001 Cancer 6 (3.4%) 49 (5.8%) 0.18 Chronic Kidney Disease 17 (9.6%) 125 (14.9%) 0.06 COPD 10 (5.6%) 39 (4.7%) 0.58 Initial laboratory findings Creatinine (μmol/L) (n=950) 75 (22-1254) 85 (20-1891) 0.001 CRP (mg/L) (n=891) 6.8 (0.3-318.9) 42.4 (0.3-444.8) 0.001 D-Dimer (mg/L FEU) (n=532) 0.97 (0.19-64.5) 0.89 (0.19-91.6) 0.86 Ferritin (μg/L) (n=623) 344.8 (9.0-28677) 590 (4.2-45878) 0.001 IL-6 (pg/mL) (n=209) 79 (15-1923) 112.5 (2-4021) 0.61 Lymphocytes (×10 9 /L) 1.72±0.74 1.44±0.75 0.001 Neutrophils (×10 9 /L) 5.7±3.6 5.8±4.1 0.91 Platelet (×10 9 /L) 250.7±83.6 231.3±85.4 0.006 Troponin (ng/L) (n=421) 20 (3-1278) 11 (3-2979) 0.50 WBC (×10 9 /L) 8.2±3.7 7.9±4.5 0.47 Platelet-to-lymphocyte ratio (PLR) 172.4±101.2 205.4±178.8 0.001 Neutrophil-to-lymphocyte ratio (NLR) 2.8 (0.6-53.0) 3.7 (0.3-72.0) 0.002 ECMO 1 (0.6%) 26 (3.1%) 0.05 Intubation 11 (6.2%) 233 (27.8%) 0.001 ICU admission 19 (10.7%) 335 (40.0%) 0.001 ICU length of stay (Days) 2.2 (0.16-45.1) 13.6 (0.1-83.4) 0.001 Ventilatory days 1.8 (0.3-5.3) 11.9 (0.1-87.8) 0.001 Complications ARDS 0 (0.0%) 31 (3.7%) 0.009 Renal Failure 9 (5.1%) 140 (16.7%) 0.001 Pulmonary embolism 0 (0.0%) 8 (1.0%) 0.19 Sepsis 2 (1.1%) 4 (0.5%) 0.30 DVT 0 (0.0%) 4 (0.5%) 0.35 Mortality 14 (7.9%) 107 (12.8%) 0.06 Table 2 summarizes the impact of age. Of the total COVID-19 patients, 74% were aged ≤55, and 26% were >55. Hypertension (75% vs. 8.1%), diabetes mellitus (DM) (70.8% vs. 27.8%), and chronic kidney disease (30.3% vs. 8.2%) were more evident in older subjects than in the younger group. Regarding vital signs, the older patients had significantly lower diastolic blood pressure (DBP), pulse rate, and oxygen saturation than the younger patients. The initial laboratory results showed that, compared with the younger patients, older patients had significantly higher NLR, PLR, creatinine, CRP, IL-6, and D-dimer levels. Intubation was performed more in older patients (42% vs.17.7%; P =0.001). Besides, the median length of ICU [14.1 (0.1-74.3) vs. 11.7 (0.16-83.4) days] and ventilatory days [13.7 (0.4-74.7) vs. 9.3 (0.1-87.8)] were significantly longer in the older group. The older patient group experienced a higher frequency of renal failure (29.2% vs. 9.6%), ARDS (4.2% vs.2.7%), pulmonary embolism (1.5% vs. 0.5%), and a higher mortality rate than the younger group (26.9% vs. 6.6 %, P <0.001). Table 2. Comparisons of clinical characteristics, complications, and outcomes among COVID-19 patients according to age. Variables Age ≤55 (n = 752, 74.0%) Age >55 (n = 264, 26.0%) P-value Age (years) 38.6±9.6 63.4±5.4 0.001 Males 564 (75.0%) 205 (77.7%) 0.38 Number of admissions 2 (1-14) 1 (1-57) 0.001 Initial vital signs Systolic blood pressure 125.2±17.5 131.6±21.6 0.001 Diastolic blood pressure 76.8±11.9 75.7±11.3 0.18 Pulse 91.3±15.8 88.8±14.7 0.02 Respiratory rate 20.2±5.1 21.3±5.4 0.003 Oxygen saturation 97.7±3.1 96.5±3.9 0.001 Comorbidities Hypertension 211 (28.1%) 198 (75.0%) 0.001 Diabetes Mellitus 209 (27.8%) 187 (70.8%) 0.001 Cancer 35 (4.7%) 20 (7.6%) 0.07 Chronic Kidney Disease 62 (8.2%) 80 (30.3%) 0.001 COPD 27 (3.6%) 22 (8.3%) 0.002 Initial laboratory findings Creatinine (μmol/L) (n=950) 80 (20-1891) 97 (32-1401) 0.001 CRP (mg/L) (n=891) 26.0 (0.3-1891) 60.3 (0.3-387.6) 0.001 D-Dimer (mg/L FEU) (n=532) 0.79 (0.19-91.6) 1.06 (0.22-84.4) 0.001 Ferritin (μg/L) (n=623) 520 (4.2-28677) 659.5 (18.3-45878) 0.001 IL-6 (pg/mL) (n=209) 94.5 (2.0-4021.0) 133 (3-2351) 0.04 Lymphocytes (×10 9 /L) 1.58±0.78 1.24±0.64 0.001 Neutrophils (×10 9 /L) 5.9±4.1 5.6±3.9 0.30 Platelet (×10 9 /L) 241.6±83.2 215.1±88.6 0.001 Troponin (ng/L) (n=421) 9 (3-2979) 19 (3-2351) 0.001 WBC (×10 9 /L) 8.3±4.5 7.4±4.1 0.006 Platelet-to-lymphocyte ratio (PLR) 194.2±168.2 214.8±167.7 0.08 Neutrophil-to-lymphocyte ratio (NLR) 3.3 (0.27-72.0) 4.0 (0.4-53.0) 0.002 ECMO 22 (2.9%) 5 (1.9%) 0.37 Intubation 133 (17.7%) 111 (42.0%) 0.001 ICU admission 205 (27.3%) 149 (56.4%) 0.001 ICU length of stay (Days) 11.7 (0.16-83.4) 14.1 (0.1-74.3) 0.06 Ventilatory days 9.3 (0.1-87.8) 13.7 (0.4-74.7) 0.04 Short stay (≤ 1 week) 152(20.2%) 26 (9.8%) 0.001 Long-stay (> 1 week) 600(79.8%) 238(90.2%) Complications ARDS 20 (2.7%) 11 (4.2%) 0.22 Renal Failure 72 (9.6%) 77 (29.2%) 0.001 Pulmonary embolism 4 (0.5%) 4 (1.5%) 0.12 Sepsis 5 (0.7%) 1 (0.4%) 0.60 Deep vein thrombosis 4 (0.5%) 0 (0.0%) 0.23 Mortality 50 (6.6%) 71 (26.9%) 0.001 Table 3 compares clinical characteristics, laboratory results, and complications among COVID-19 patients stratified according to survival status. The deceased patients were significantly older than those who survived (56.4±11.4 vs. 43.5±13.25 years, respectively, P <0.001) with more comorbidities as well. Creatinine, CRP, D-dimer, ferritin, IL-6, neutrophil, troponin, PLR, and NLR were significantly higher, whereas lymphocyte and platelet counts were significantly lower in the deceased patients. Table 3. Comparisons of clinical characteristics, complications, and outcomes among COVID-19 patients according to mortality. Variables Survivors (n=895) Non-survivors (n=121) P -value Age (years) 43.5±13.5 56.4±11.4 0.001 Males 664 (74.2%) 105 (86.8%) 0.002 Comorbidities Hypertension 336 (37.5%) 73 (60.3%) 0.001 Diabetes Mellitus 318 (35.5%) 78 (64.5%) 0.001 Cancer 43 (4.8%) 12 (9.9%) 0.02 Chronic Kidney Disease 115 (12.8%) 27 (22.3%) 0.005 COPD 43 (4.8%) 6 (5.0%) 0.94 Initial laboratory findings Creatinine (μmol/L) (n=950) 82 (20-1891) 101 (32-1131) 0.001 CRP (mg/L) (n=891) 28.0 (0.3-444.8) 94.2 (0.4-387.6) 0.001 D-Dimer (mg/L FEU) (n=532) 0.82 (0.19-91.6) 1.24 (0.3-84.4) 0.001 Ferritin (μg/L) (n=623) 527 (4.2-45878) 868.5 (66.5-39695) 0.001 IL-6 (pg/mL) (n=209) 87 (2-4021) 185.5 (4-2599) 0.006 Lymphocytes (×10 9 /L) 1.55±0.77 1.03±0.52 0.001 Neutrophils (×10 9 /L) 5.7±3.8 7.0±5.3 0.008 Platelet (×10 9 /L) 238.6±83.9 206.2±91.1 0.001 Troponin (ng/L) (n=421) 9 (3-2351) 27 (3-2979) 0.001 WBC (×10 9 /L) 117 (36.1%) 65 (67.0%) 0.001 Platelet-to-lymphocyte ratio (PLR) 192.2±158.5 253.9±221.1 0.004 Neutrophil-to-lymphocyte ratio (NLR) 3.2 (0.27-72.0) 5.8 (0.9-53.0) 0.001 ICU length of stay (days) 10.9 (0.1-72) 16.6 (0.16-83.4) 0.001 Hospital length of stay (days) 19.6 (0.13-153) 20.7(0.61-89.4) 0.002 Ventilatory days 8.0 (0.1-78.5) 16.4 (0.3-87.8) 0.001 Complications ARDS 16 (1.8%) 15 (12.4%) 0.001 Renal failure 90 (10.1%) 59 (48.8%) 0.001 Pulmonary embolism 5 (0.6%) 3 (2.5%) 0.02 Sepsis 4 (0.4%) 2 (1.7%) 0.10 Deep vein thrombosis 4 (0.4%) 0 (0.0%) 0.46 Figure 1(a) and (b) show the result of the ROC analysis plotting the sensitivity and specificity of the PLR and NLR and their discriminatory ability to predict overall mortality and their performance by age categories in COVID-19 patients, respectively. Figure 1. Receiver operating characteristic (ROC) curves analyses for predicting discriminatory power analysis of initial Platelet-to-Lymphocyte Ratio and Neutrophil-to-Lymphocyte Ratio for the prediction of mortality in COVID-19 patients (a) overall mortality (b) mortality by age groups. The area under the curve (AUC) for NLR was 0.710, indicating a good discriminatory performance, and for PLR, 0.614 suggesting a fair discriminatory capacity, respectively. The optimal cut-off for NLR and PLR were 5.03 (Sensitivity 66.9% and specificity 46.5%) and 150.16 (Sensitivity 61.2% and specificity 68.4%). In individuals aged ≤55 years, the PLR demonstrated moderate discrimination with an AUC of 0.620, while the NLR exhibited a higher AUC of 0.717, signifying superior discrimination compared to the PLR. Conversely, for individuals aged >55 years, PLR showed an increased higher AUC of 0.710 in comparison to those ≤55 years, implying enhanced discrimination in this age group, while NLR exhibited moderate discrimination with an AUC of 0.693 ( Figure 1(b) ). Table 4 shows the association of PLR and NLR in predicting mortality and delayed recovery in COVID-19 patients. The crude odd ratio for NLR was 1.078 (95% CI 1.049-1.109; P =0.001), and PLR was 1.001 (95% CI, 1.001-1.002; P =0.002) for mortality. The crude odd ratio for NLR was 1.034 (95% CI 0.996-1.072; P =0.078), and PLR was 1.002 (95% CI, 1.000-1.004; P =0.021) for delayed recovery. Table 4. Association of PLR and NLR with mortality and delayed recovery. Variables Crude Odd ratio 95% CI P value Lower Upper Mortality Platelet-to-lymphocyte ratio 1.001 1.001 1.002 0.002 Neutrophil-to-lymphocyte ratio 1.078 1.049 1.109 0.001 Delayed recovery (HLOS >7 days) Platelet-to-lymphocyte ratio 1.002 1.000 1.004 0.021 Neutrophil-to-lymphocyte ratio 1.034 0.996 1.072 0.078 Table 5 depicts the results of multivariate regression analysis to determine independent predictors of mortality. After adjusting for the relevant covariates, being older than 55 years (OR 1.068; 95% CI 1.045 to 1.091; P =0.001), hypertension (OR 0.437; 95% CI 0.255 to 0.751; P =0.003), diabetes mellitus, (OR 1.730; 95% CI 1.050 to 2.851; P =0.032), CRP (OR 1.004; 95% CI 1.002 to 1.007; P =0.001), and renal failure (OR 6.620; 95% CI 3.989 to 10.989; P =0.001) were found to be independent predictors of mortality. However, NLR (OR 1.039; 95% CI 0.998 to 1.082; P =0.051) and PLR (OR 1.000; 95% CI 0.998 to 1.001; P =0.581) were not independently associated with in-hospital mortality. Table 5. Multivariate regression analysis for predictors of mortality. Variables Odd Ratio 95% CI P value Lower Upper Age 1.068 1.045 1.091 0.001 Males 1.125 0.595 2.129 0.717 Hypertension 0.437 0.255 0.751 0.003 Diabetes Mellitus 1.730 1.050 2.851 0.032 C- reactive protein (CRP) 1.004 1.002 1.007 0.001 Platelet to lymphocyte ratio (PLR) 1.000 0.998 1.001 0.581 Neutrophil to lymphocyte ratio (NLR) 1.039 0.998 1.082 0.051 Renal Failure 6.620 3.989 10.989 0.001 Discussion Ever since the first cases of the COVID-19 pandemic were reported, healthcare institutions have worked to develop diagnostic tools and prognostic indications. The current study investigates associations between NLR, PLR, age, duration of hospital length of stay, and mortality in COVID-19 patients in Qatar. The current study provides unique insights into biomarker behavior within a younger, predominantly male cohort (mean age 45±13.9 years), a demographic underrepresented in earlier studies that focused largely on elderly or critically ill populations. Stratified analysis revealed that NLR exhibited greater discriminatory ability for mortality in patients aged ≤55 years (AUC=0.717), whereas PLR performed better in those aged >55 years (AUC=0.710). These age-dependent differences in performance, not well documented in previous literature, suggest a need for tailored interpretation of these markers across age groups. Moreover, In contrast to most existing studies that focus solely on mortality or ICU admission, we also evaluated the association between NLR/PLR and hospital length of stay. The study demonstrated that patients with higher admission NLR and PLR levels were associated with delayed recovery, more intubation, and ICU admissions. In contrast, an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients than PLR. Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world. 28 In our analysis of 1016 COVID-19 patients, the overall in-hospital mortality was 11.9%. The mortality rate was lower than those reported in previous studies from other countries. 29 , 30 Notably, lower mortality was also observed in the elderly population (aged >55). Several studies and meta-analyses have concluded that the predictive value of NLR and PLR could be used to stratify COVID-19 patients, and especially high NLR at admission has been associated with poor outcomes. 11 , 12 , 30 – 32 While previous publications included older adults, they did not perform a subgroup analysis to assess the estimated mortality risk for this age group. Ciccullo et al. demonstrated that younger age, and NLR below 3 were associated with clinical improvement, while NLR over 4 predicted ICU transfer. 31 Based upon the ROC analysis, the cut-off values for predicting mortality were 5.03 for NLR, and 150.16 for PLR. This result is in concordance with previous studies, in which the proposed optimum cut-off values for NLR ranged from 3 to 6, 11 , 32 , 33 and cut-off PLR values were between 140-160. 32 , 34 Liu et al. showed that older patients (>50 years old) with NLR ≥3.13 are more likely to develop a critical illness. 35 Yang et al. found that elevated NLR and advanced age were associated with severe COVID-19 illness and independently predicted the worse clinical outcomes. 36 The NLR has emerged as a potent inflammatory marker with diagnostic and prognostic utility in various clinical conditions. 11 – 13 , 15 , 16 , 21 , 22 , 35 , 37 – 39 NLR represents the equilibrium of innate and adaptive immune responses. 18 A high NLR implies an aberrant immune response, characterized by increased neutrophils and decreased lymphocytes. Neutrophil production can also be augmented by virus-induced inflammatory factors such as IL-6, Interleukin-8 (IL-8), and tumor necrosis factor α (TNF- α). 4 , 37 , 40 Furthermore, it appears to be a more reliable technique than PLR, absolute neutrophils, and lymphocyte counts, as it is less influenced by confounders. The current study’s asynchronous pattern between NLR and PLR highlights that both ratios were elevated during the onset of the COVID-19 disease, but NLR continued to increase afterward, especially in older individuals. This suggests that NLR offers additional information regarding the ongoing inflammatory state in COVID-19 patients, especially those with poor prognoses. Our results suggest that NLR can be a more valuable predictor of poor prognosis across the different sub-categories of patients studied in the current study. However, neither NLR nor PLR was shown to be independent predictor of mortality on multivariate analysis, contrasting with previous reports. 1 , 14 , 24 , 31 , 38 , 41 , 42 This discrepancy could be explained by one of two factors. First, the pathogenesis of SARS-CoV-2 infection is complicated. Secondly, this could be attributed to the small sample size reported in the previous studies. The severity of infections, hematological derangements (NLR, PLR), and mortality increased sharply with age. This trend was particularly evident for critical infection, in-hospital complications, and mortality, which remained low in patients under 50 but increased rapidly in those over 50. It has been well established that patients of advanced age are more susceptible to COVID-19 mortality. 43 – 46 Regarding the clinical outcome of patients based on recovery time, we found that delayed recovery (HLOS> seven days) was associated with advanced age, prolonged ICU stay, requiring mechanical ventilation, and higher mortality. A significantly higher proportion of the older population with prolonged HLOS had comorbidities, suggesting that advanced age and associated comorbidities require necessitate longer hospitalization and confer a greater risk of mortality. 47 Moreover, a higher mean PLR in our cohort was significantly associated with delayed recovery. While NLR showed a modest and suggestive increase in the odds of delayed recovery, the association was non-significant. Studies have shown that HLOS is age-dependent. 48 We could not find studies evaluating the impact of NLR and PLR as prognostic markers of early versus delayed recovery. Limitations Some limitations may have affected the study and warrant consideration. This retrospective analysis did not document the patient’s follow-up. Additionally, while input data were most complete at the national level, but the generalizability of the results may be constrained by variations within Qatar’s highly diversified population. In addition, the selection bias, and power of the study cannot be ignored; a larger sample would better size to reflect the prognostic significance of NLR/PLR in the prognosis of patients with COVID-19. Furthermore, the cycle threshold (Ct) value has been proposed as a potential prognostic indicator in patients with COVID-19. Although Ct data were unavailable in this study, combining NLR/PLR with Ct findings in future research could refine the prognostic evaluation of COVID-19 patients. Another notable limitation of our study was the lack of data on respiratory function parameters, specifically arterial oxygenation indices such as the PaO 2 /FiO 2 ratio. Since hypoxemia is a key determinant of disease progression and mortality, the absence of such data may have limited our ability to fully adjust for respiratory status in the multivariable models or correlate inflammatory markers with the degree of pulmonary dysfunction. In addition, the lack of consistent documentation on types of ventilatory support modalities (non-invasive versus invasive ventilation) limited our ability to correlate inflammatory markers with the degree of pulmonary dysfunction and disease severity. Lastly, a prospective study should ideally test the predictive value of NLR and PLR longitudinally. Despite these limitations, our study tailored to the complexity of the early pandemic period, reproduces the key biochemical trends, and offers valuable insights into the prognostic utility of NLR and PLR in COVID-19 patients. Although neither marker emerged as an independent predictor of mortality in multivariable analysis, their early association with adverse outcomes supports their role as practical tools for initial risk stratification. The absence of independent predictive value likely reflects the multifactorial nature of COVID-19 progression, influenced by host factors such as age, comorbidities, and immune response. Taken together, our findings contribute timely evidence from the initial phase of the pandemic and underscore the nuanced role of inflammation-based hematological markers in predicting not only mortality but also recovery patterns and healthcare needs, particularly in younger or ethnically diverse populations. Conclusion In this retrospective study conducted during the early peak of the COVID-19 pandemic, we demonstrate that admission levels of NLR and PLR are associated with disease severity and clinical outcomes in a predominantly young and ethnically diverse population. While neither marker independently predicted mortality in multivariable analysis, patients with higher NLR and PLR levels were associated with delayed recovery, ICU admissions, and intubation, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. However, none of these two parameters was found to be an independent predictor for death. These findings highlight the potential utility of NLR and PLR as accessible, cost-effective tools for early risk stratification, particularly in resource-limited or surge settings. Ethics and consent This study was approved by the Research Ethics Committee of the Medical Research Center, Hamad Medical Corporation (HMC), Doha, Qatar (MRC-01-20-672 & MRC-05-213) on 29 Sep 2020. A waiver of consent was granted for this retrospective study, as there was no direct contact was made with the participants, and the data were collected anonymously. Authors’ contributions All authors have substantially contributed to the acquisition, analysis, and interpretation of data for the work, drafting the work or revising it critically for important intellectual content, and final approval of the version to be published. Data availability As the COVID-19 data are owned by the medical research center and CDC department, We have provided the email and condition by which the reader can access the de-identified data. All data are presented in the manuscript including tables and de-identified data can be accessed after a permission from the medical research center of HMC, Qatar ( [email protected] ), on a reasonable research request. Software availability For statistical analysis, we purchased a license to use software available from: https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-28010 . References 1. Sarkar S, Kannan S, Khanna P, et al. : Role of platelet-to-lymphocyte count ratio (PLR), as a prognostic indicator in COVID-19: A systematic review and meta-analysis. J. Med. Virol. 2022; 94 : 211–221. PubMed Abstract | Publisher Full Text | Free Full Text 2. García LF: Immune Response, Inflammation, and the Clinical Spectrum of COVID-19. Front. Immunol. 2020; 11 : 1441. PubMed Abstract | Publisher Full Text | Free Full Text 3. Tay MZ, Poh CM, Rénia L, et al. : The trinity of COVID-19: immunity, inflammation and intervention. Nat. Rev. Immunol. 2020; 20 : 363–374. PubMed Abstract | Publisher Full Text | Free Full Text 4. Mortaz E, Tabarsi P, Varahram M, et al. : The Immune Response and Immunopathology of COVID-19. Front. Immunol. 2020; 11 : 2037. PubMed Abstract | Publisher Full Text | Free Full Text 5. Zahorec R: Neutrophil-to-lymphocyte ratio, past, present, and future perspectives. Bratisl. Lek. Listy. 2021; 122 : 474–488. PubMed Abstract | Publisher Full Text 6. Buonacera A, Stancanelli B, Colaci M, et al. : Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int. J. Mol. Sci. 2022; 23 : 23. Publisher Full Text 7. Wang G, Mivefroshan A, Yaghoobpoor S, et al. : Prognostic Value of Platelet to Lymphocyte Ratio in Sepsis: A Systematic Review and Meta-analysis. Biomed. Res. Int. 2022; 2022 : 9056363. 8. El-Menyar A, Mekkodathil A, Al-Ansari A, et al. : Platelet-Lymphocyte and Neutrophil-Lymphocyte Ratio for Prediction of Hospital Outcomes in Patients with Abdominal Trauma. Biomed. Res. Int. 2022; 2022 : 5374419. 9. Al-Yahri O, Saafan T, Abdelrahman H, et al. : Platelet to Lymphocyte Ratio Associated with Prolonged Hospital Length of Stay Postpeptic Ulcer Perforation Repair: An Observational Descriptive Analysis. Biomed. Res. Int. 2021; 2021 : 6680414. 10. Wang W, Zhao Z, Liu X, et al. : Clinical features and potential risk factors for discerning the critical cases and predicting the outcome of patients with COVID-19. J. Clin. Lab. Anal. 2020; 34 : e23547. PubMed Abstract | Publisher Full Text | Free Full Text 11. Liu Y, Du X, Chen J, et al. : Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. J. Infect. 2020; 81 : e6–e12. PubMed Abstract | Publisher Full Text 12. Sarkar PG, Pant P, Kumar J, et al. : Does Neutrophil-to-lymphocyte Ratio at Admission Predict Severity and Mortality in COVID-19 Patients? A Systematic Review and Meta-analysis. Indian J. Crit. Care Med. 2022; 26 : 361–375. PubMed Abstract | Publisher Full Text 13. Regolo M, Vaccaro M, Sorce A, et al. : Neutrophil-to-Lymphocyte Ratio (NLR) Is a Promising Predictor of Mortality and Admission to the Intensive Care Unit of COVID-19 Patients. J. Clin. Med. 2022; 11 : 11. Publisher Full Text 14. Shahid MF, Malik A, Siddiqi FA, et al. : Neutrophil-to-Lymphocyte Ratio and Absolute Lymphocyte Count as Early Diagnostic Tools for Corona Virus Disease 2019. Cureus. 2022; 14 : e22863. Publisher Full Text 15. La Torre G, Marte M, Massetti AP, et al. : The neutrophil/lymphocyte ratio as a prognostic factor in COVID-19 patients: a case-control study. Eur. Rev. Med. Pharmacol. Sci. 2022; 26 : 1056–1064. PubMed Abstract | Publisher Full Text 16. Rose J, Suter F, Furrer E, et al. : Neutrophile-to-Lymphocyte Ratio (NLR) Identifies Patients with Coronavirus Infectious Disease 2019 (COVID-19) at High Risk for Deterioration and Mortality-A Retrospective, Monocentric Cohort Study. Diagnostics (Basel). 2022; 12 . Publisher Full Text 17. Fois AG, Paliogiannis P, Scano V, et al. : The Systemic Inflammation Index on Admission Predicts In-Hospital Mortality in COVID-19 Patients. Molecules. 2020; 25 . PubMed Abstract | Publisher Full Text | Free Full Text 18. Zeng F, Deng G, Cui Y, et al. : A predictive model for the severity of COVID-19 in elderly patients. Aging (Albany NY). 2020; 12 : 20982–20996. PubMed Abstract | Publisher Full Text | Free Full Text 19. Gong J, Ou J, Qiu X, et al. : A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China. Clin. Infect. Dis. 2020; 71 : 833–840. PubMed Abstract | Publisher Full Text | Free Full Text 20. Zhu Z, Cai T, Fan L, et al. : Clinical value of immune-inflammatory parameters to assess the severity of coronavirus disease 2019. Int. J. Infect. Dis. 2020; 95 : 332–339. PubMed Abstract | Publisher Full Text | Free Full Text 21. Erdogan A, Can FE, Gönüllü H: Evaluation of the prognostic role of NLR, LMR, PLR, and LCR ratio in COVID-19 patients. J. Med. Virol. 2021; 93 : 5555–5559. PubMed Abstract | Publisher Full Text | Free Full Text 22. Fors M, Ballaz S, Ramírez H, et al. : Sex-Dependent Performance of the Neutrophil-to-Lymphocyte, Monocyte-to-Lymphocyte, Platelet-to-Lymphocyte, and Mean Platelet Volume-to-Platelet Ratios in Discriminating COVID-19 Severity. Front. Cardiovasc Med. 2022; 9 : 822556. PubMed Abstract | Publisher Full Text | Free Full Text 23. Azab B, Camacho-Rivera M, Taioli E: Average values and racial differences of neutrophil-lymphocyte ratio among a nationally representative sample of United States subjects. PLoS One. 2014; 9 : e112361. PubMed Abstract | Publisher Full Text | Free Full Text 24. Vafadar Moradi E, Teimouri A, Rezaee R, et al. : Increased age, neutrophil-to-lymphocyte ratio (NLR), and white blood cell count are associated with higher COVID-19 mortality. Am. J. Emerg. Med. 2021; 40 : 11–14. PubMed Abstract | Publisher Full Text | Free Full Text 25. Ayoub HH, Chemaitelly H, Seedat S, et al. : Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide. PLoS One. 2020; 15 : e0237959. PubMed Abstract | Publisher Full Text | Free Full Text 26. Wu C, Chen X, Cai Y, 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 : 934–943. PubMed Abstract | Publisher Full Text | Free Full Text 27. Zhou F, Yu T, Du R, et al. : Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395 : 1054–1062. PubMed Abstract | Publisher Full Text | Free Full Text 28. Khatib MY, Ananthegowda DC, Elshafei MS, et al. : Predictors of mortality and morbidity in critically ill COVID-19 patients: An experience from a low mortality country. Health Sci. Rep. 2022; 5 : e542. PubMed Abstract | Publisher Full Text | Free Full Text 29. McPadden J, Warner F, Young HP, et al. : Clinical characteristics and outcomes for 7,995 patients with SARS-CoV-2 infection. PLoS One. 2021; 16 : e0243291. PubMed Abstract | Publisher Full Text | Free Full Text 30. Baggio JAO, Machado MF, Carmo RFD, et al. : COVID-19 in Brazil: spatial risk, social vulnerability, human development, clinical manifestations and predictors of mortality - a retrospective study with data from 59 695 individuals. Epidemiol. Infect. 2021; 149 : e100. PubMed Abstract | Publisher Full Text | Free Full Text 31. Ciccullo A, Borghetti A, Zileri Dal Verme L, et al. : Neutrophil-to-lymphocyte ratio and clinical outcome in COVID-19: a report from the Italian front line. Int. J. Antimicrob. Agents. 2020; 56 : 106017. PubMed Abstract | Publisher Full Text | Free Full Text 32. Güneysu F, Guner NG, Erdem AF, et al. : Can COVID-19 Mortality be Predicted in the Emergency Room? J. Coll. Physicians Surg. Pak. 2020; 30 : 928–932. PubMed Abstract | Publisher Full Text 33. Yildiz H, Castanares-Zapatero D, Pierman G, et al. : Validation of Neutrophil-to-Lymphocyte Ratio Cut-off Value Associated with High In-Hospital Mortality in COVID-19 Patients. Int. J. Gen. Med. 2021; 14 : 5111–5117. PubMed Abstract | Publisher Full Text | Free Full Text 34. Asghar MS, Haider Kazmi SJ, Ahmed Khan N, et al. : Clinical Profiles, Characteristics, and Outcomes of the First 100 Admitted COVID-19 Patients in Pakistan: A Single-Center Retrospective Study in a Tertiary Care Hospital of Karachi. Cureus. 2020; 12 : e8712. 35. Liu J, Liu Y, Xiang P, et al. : Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J. Transl. Med. 2020; 18 : 206. PubMed Abstract | Publisher Full Text | Free Full Text 36. Yang AP, Liu JP, Tao WQ, et al. : The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int. Immunopharmacol. 2020; 84 : 106504. PubMed Abstract | Publisher Full Text | Free Full Text 37. Luo H, He L, Zhang G, et al. : Normal Reference Intervals of Neutrophil-To-Lymphocyte Ratio, Platelet-To-Lymphocyte Ratio, Lymphocyte-To-Monocyte Ratio, and Systemic Immune Inflammation Index in Healthy Adults: a Large Multi-Center Study from Western China. Clin. Lab. 2019; 65 . PubMed Abstract | Publisher Full Text 38. Seyit M, Avci E, Nar R, et al. : Neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio and Platelet to lymphocyte ratio to predict the severity of COVID-19. Am. J. Emerg. Med. 2021; 40 : 110–114. PubMed Abstract | Publisher Full Text | Free Full Text 39. Toori KU, Qureshi MA, Chaudhry A, et al. : Neutrophil to lymphocyte ratio (NLR) in COVID-19: A cheap prognostic marker in a resource constraint setting. Pak. J. Med. Sci. 2021; 37 : 1435–1439. PubMed Abstract | Publisher Full Text 40. Sawa T, Akaike T: What triggers inflammation in COVID-19? elife. 2022; 11 : 11. Publisher Full Text 41. Grasselli G, Greco M, Zanella A, et al. : Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern. Med. 2020; 180 : 1345–1355. PubMed Abstract | Publisher Full Text | Free Full Text 42. Tatum D, Taghavi S, Houghton A, et al. : Neutrophil-to-Lymphocyte Ratio and Outcomes in Louisiana COVID-19 Patients. Shock. 2020; 54 : 652–658. PubMed Abstract | Publisher Full Text | Free Full Text 43. Xu G, Ye M, Zhao J, et al. : New view on older adults with COVID-19: comments on “SARS-CoV-2 and COVID-19 in older adults: what we may expect regarding pathogenesis, immune responses, and outcomes”. Geroscience. 2020; 42 : 1225–1227. PubMed Abstract | Publisher Full Text | Free Full Text 44. Gosch M, Singler K, Kwetkat A, et al. : COVID-19 in older adults - a complex challenge. MMW Fortschr. Med. 2020; 162 : 36–40. PubMed Abstract | Publisher Full Text | Free Full Text 45. Tan X, Zhang S, Xu J, et al. : Comparison of clinical characteristics among younger and elderly deceased patients with COVID-19: a retrospective study. Aging (Albany NY). 2020; 13 : 16–26. PubMed Abstract | Publisher Full Text 46. Pepe M, Maroun-Eid C, Romero R, et al. : Clinical presentation, therapeutic approach, and outcome of young patients admitted for COVID-19, with respect to the elderly counterpart. Clin. Exp. Med. 2021; 21 : 249–268. PubMed Abstract | Publisher Full Text | Free Full Text 47. da Costa Sousa V , da Silva MC , de Mello MP , et al. : Factors associated with mortality, length of hospital stay and diagnosis of COVID-19: Data from a field hospital. J. Infect. Public Health. 2022; 15 : 800–805. 48. Zheng H, Tan J, Zhang X, et al. : Impact of sex and age on respiratory support and length of hospital stay among 1792 patients with COVID-19 in Wuhan, China. Br. J. Anaesth. 2020; 125 : e378–e380. PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 03 May 2024 ADD YOUR COMMENT Comment Author details Author details 1 Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Doha, Qatar 2 Clinical Medicine, Weill Cornell Medical College, Doha, Qatar 3 Department of Surgery, Hamad Medical Corporation, Doha, Doha, Qatar 4 Department of Medicine, Hamad Medical Corporation, Doha, Doha, Qatar Ayman El-Menyar Roles: Conceptualization, Methodology, Writing – Original Draft Preparation Naushad A. Khan Roles: Data Curation, Formal Analysis, Writing – Review & Editing Mohammad Asim Roles: Data Curation, Formal Analysis, Methodology Hassan Al-Thani Roles: Methodology, Writing – Review & Editing Mohammed Abukhattab Roles: Conceptualization, Methodology Muna Al Maslamani Roles: Conceptualization, Methodology Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 07 Jul 2025, 13:446 https://doi.org/10.12688/f1000research.146814.2 version 1 Published: 03 May 2024, 13:446 https://doi.org/10.12688/f1000research.146814.1 Copyright © 2025 El-Menyar A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article El-Menyar A, Khan NA, Asim M et al. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.12688/f1000research.146814.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 07 Jul 2025 Revised Views 0 Cite How to cite this report: Isaia I and Malatino L. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.184107.r397253 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v2#referee-response-397253 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Nov 2025 Ivan Isaia , Department of Clinical and Experimental Medicine, University of Catania, Clinica Medica-Ospedale Cannizzaro, Catania, Italy; Clinical and experimental medicine, University of Catania, Catania, Catania, Italy Lorenzo Malatino , Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.184107.r397253 I revised the second version of this article. Two references I suggested to include in the reference list are not yet acknowledged: 1) Cataudella E. et al., 2017: 2) Regolo M. et al., J Clin Med 2023. ... Continue reading READ ALL I revised the second version of this article. Two references I suggested to include in the reference list are not yet acknowledged: 1) Cataudella E. et al., 2017: 2) Regolo M. et al., J Clin Med 2023. Both references support my comments included in my first report and specifically regard the clinical frame within this paper by El-Menyar actually falls. Furhermore, I strongly disagree, by the way, with the assumption concluding Authors' rebuttal \"Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work\". In fact, papers addressed to. clinical audience should always focus in nature on their impact on the pathooenetic chain of the disease. state they are related to.. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Internal and Clinical Medicine, Cardiology, Neutrophil-to-Lymphocyte Ratio, Infectious diseases, Covid-19 disease. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Isaia I and Malatino L. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.184107.r397253 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v2#referee-response-397253 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Paganelli R. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.184107.r397254 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v2#referee-response-397254 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Jul 2025 Roberto Paganelli , International Medical University in Rome, UniCamillus, Rome, Italy Approved VIEWS 0 https://doi.org/10.5256/f1000research.184107.r397254 The article is acceptable ... Continue reading READ ALL The article is acceptable now. I recommend Indexing. Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Paganelli R. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.184107.r397254 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v2#referee-response-397254 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 Nov 2025 Ayman El-Menyar , Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Qatar 05 Nov 2025 Author Response Thank you. Competing Interests: No competing interests were disclosed. Thank you. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 Nov 2025 Ayman El-Menyar , Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Qatar 05 Nov 2025 Author Response Thank you. Competing Interests: No competing interests were disclosed. Thank you. Thank you. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 03 May 2024 Views 0 Cite How to cite this report: Paganelli R. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.160935.r385519 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v1#referee-response-385519 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Jun 2025 Roberto Paganelli , International Medical University in Rome, UniCamillus, Rome, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.160935.r385519 This is a retrospective study of COVID-19 patients' outcome at a group of Hospitals in Qatar in 2020, at the beginning of the pandemic. The authors seeked to evaluate the association of NLR and PLR at admission with mortality and ... Continue reading READ ALL This is a retrospective study of COVID-19 patients' outcome at a group of Hospitals in Qatar in 2020, at the beginning of the pandemic. The authors seeked to evaluate the association of NLR and PLR at admission with mortality and severity of patients. The results show that NLR predicts mortality in all cases, and PLR is better for length of hospital stay. These parameters however are non independent predictors, and older subjects had a worse outcome. Most of this was known since the beginning of the pandemic, so it adds very little to existing literature. Moreover, the study does not mention the ethnic origin of the patients, who were mostly males. It may deserve publication as a record of the pandemic viewed from a small country with large immigration. The English language needs improvement. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: immunology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Paganelli R. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.160935.r385519 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v1#referee-response-385519 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Malatino L and Isaia I. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.160935.r282772 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v1#referee-response-282772 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Jun 2024 Lorenzo Malatino , Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy Ivan Isaia , Department of Clinical and Experimental Medicine, University of Catania, Clinica Medica-Ospedale Cannizzaro, Catania, Italy; Clinical and experimental medicine, University of Catania, Catania, Catania, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.160935.r282772 This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU ... Continue reading READ ALL This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome. In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so making difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid-19. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Regolo M, Vaccaro M, Sorce A, Stancanelli B, et al.: Neutrophil-to-Lymphocyte Ratio (NLR) Is a Promising Predictor of Mortality and Admission to Intensive Care Unit of COVID-19 Patients. J Clin Med . 2022; 11 (8). PubMed Abstract | Publisher Full Text 2. Cataudella E, Giraffa CM, Di Marca S, Pulvirenti A, et al.: Neutrophil-To-Lymphocyte Ratio: An Emerging Marker Predicting Prognosis in Elderly Adults with Community-Acquired Pneumonia. J Am Geriatr Soc . 2017; 65 (8): 1796-1801 PubMed Abstract | Publisher Full Text 3. Buonacera A, Stancanelli B, Colaci M, Malatino L: Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int J Mol Sci . 2022; 23 (7). PubMed Abstract | Publisher Full Text 4. Regolo M, Sorce A, Vaccaro M, Colaci M, et al.: Assessing Humoral Immuno-Inflammatory Pathways Associated with Respiratory Failure in COVID-19 Patients. J Clin Med . 2023; 12 (12). PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Internal and Clinical Medicine, Cardiology, Neutrophil-to-Lymphocyte Ratio We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Malatino L and Isaia I. Reviewer Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.160935.r282772 ) The direct URL for this report is: https://f1000research.com/articles/13-446/v1#referee-response-282772 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 07 Jul 2025 Ayman El-Menyar , Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Qatar 07 Jul 2025 Author Response Reviewer 1 Query: This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: ... Continue reading Reviewer 1 Query: This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome. In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so it makes it difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid19. Reply to reviewer-1 I would like to thank the reviewer for their thoughtful comments and for engaging with our research. We appreciate the opportunity to address your queries and provide further clarification on our findings. Regarding the discrepancy between our study and the previous findings by Regolo et al. on the predictive value of NLR for mortality in COVID-19 patients, we acknowledge the importance of understanding the nuances in different patient cohorts and disease presentations. While our study highlights the association of NLR and PLR with hospitalization duration and recovery based on the patient's age, the findings interestingly showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. In our study, univariate analysis showed that admission NLR levels significantly differed between survivors and non-survivors [5.8 (0.9-53.0) vs. 3.2 (0.27-72.0); p=0.001]. Consistent with Regolo et al., our data showed an association of NLR with predicting mortality and delayed recovery in COVID-19 patients. The crude odds ratio for NLR was 1.078 (95% CI 1.049-1.109; p=0.001) for mortality and 1.034 (95% CI 0.996-1.072; p=0.078) for delayed recovery. However, the independent association of NLR (OR 1.039; 95% CI 0.998-1.082; p=0.051) with mortality relatively disappeared in our multivariate model after adjusting for age and other relevant covariates. Though our data suggest a clinically relevant OR of 1.039, it failed to reach statistical significance (p=0.051). In the context of multivariate analysis, collinearity with other variables can diminish the independent predictive power of NLR. This may be due to the fact that NLR, PLR, and CRP, to some extent, are associated with the same pathophysiological chain, i.e., inflammation. Their interaction means that CRP might account for most of the variance related to inflammation, reducing the unique contribution of NLR. Similarly, PLR is another marker of inflammation and immune response, and its inclusion alongside NLR can lead to redundancy, with one ratio (PLR or NLR) not adding significant predictive value over the other. Additionally, larger sample sizes in multivariate analysis can increase the model's complexity, requiring careful consideration of potential confounders and interactions between variables. The specific observed differences between our study and Regolo et al.'s findings could be attributed to various factors, including differences in patient demographics, such as age, disease severity, methodology, and sample size. Our study focuses on a specific cohort of young patients during the initial phase of the pandemic, which may have unique characteristics compared to the older patient populations studied by Regolo et al. (the median age of the patient population was 72). Moreover, our cohort sample size was 1016 compared to 411 in Regolo et al.'s study. As sample sizes increase, the effect sizes of variables may become smaller, and statistically significant associations may be observed with smaller magnitudes of effect. This phenomenon can sometimes lead to a perception that the associations are less clinically meaningful. Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world. The overall in-hospital mortality was 11.9%, lower than the 19.5% reported by Regolo et al. Lower mortality was also observed in the elderly population (aged >55). Lastly, we acknowledge the lack of data on respiratory function in our study. We believe that the objectives of our study were clearly defined and focused. Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work. Reviewer 1 Query: This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome. In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so it makes it difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid19. Reply to reviewer-1 I would like to thank the reviewer for their thoughtful comments and for engaging with our research. We appreciate the opportunity to address your queries and provide further clarification on our findings. Regarding the discrepancy between our study and the previous findings by Regolo et al. on the predictive value of NLR for mortality in COVID-19 patients, we acknowledge the importance of understanding the nuances in different patient cohorts and disease presentations. While our study highlights the association of NLR and PLR with hospitalization duration and recovery based on the patient's age, the findings interestingly showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. In our study, univariate analysis showed that admission NLR levels significantly differed between survivors and non-survivors [5.8 (0.9-53.0) vs. 3.2 (0.27-72.0); p=0.001]. Consistent with Regolo et al., our data showed an association of NLR with predicting mortality and delayed recovery in COVID-19 patients. The crude odds ratio for NLR was 1.078 (95% CI 1.049-1.109; p=0.001) for mortality and 1.034 (95% CI 0.996-1.072; p=0.078) for delayed recovery. However, the independent association of NLR (OR 1.039; 95% CI 0.998-1.082; p=0.051) with mortality relatively disappeared in our multivariate model after adjusting for age and other relevant covariates. Though our data suggest a clinically relevant OR of 1.039, it failed to reach statistical significance (p=0.051). In the context of multivariate analysis, collinearity with other variables can diminish the independent predictive power of NLR. This may be due to the fact that NLR, PLR, and CRP, to some extent, are associated with the same pathophysiological chain, i.e., inflammation. Their interaction means that CRP might account for most of the variance related to inflammation, reducing the unique contribution of NLR. Similarly, PLR is another marker of inflammation and immune response, and its inclusion alongside NLR can lead to redundancy, with one ratio (PLR or NLR) not adding significant predictive value over the other. Additionally, larger sample sizes in multivariate analysis can increase the model's complexity, requiring careful consideration of potential confounders and interactions between variables. The specific observed differences between our study and Regolo et al.'s findings could be attributed to various factors, including differences in patient demographics, such as age, disease severity, methodology, and sample size. Our study focuses on a specific cohort of young patients during the initial phase of the pandemic, which may have unique characteristics compared to the older patient populations studied by Regolo et al. (the median age of the patient population was 72). Moreover, our cohort sample size was 1016 compared to 411 in Regolo et al.'s study. As sample sizes increase, the effect sizes of variables may become smaller, and statistically significant associations may be observed with smaller magnitudes of effect. This phenomenon can sometimes lead to a perception that the associations are less clinically meaningful. Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world. The overall in-hospital mortality was 11.9%, lower than the 19.5% reported by Regolo et al. Lower mortality was also observed in the elderly population (aged >55). Lastly, we acknowledge the lack of data on respiratory function in our study. We believe that the objectives of our study were clearly defined and focused. Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 07 Jul 2025 Ayman El-Menyar , Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Qatar 07 Jul 2025 Author Response Reviewer 1 Query: This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: ... Continue reading Reviewer 1 Query: This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome. In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so it makes it difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid19. Reply to reviewer-1 I would like to thank the reviewer for their thoughtful comments and for engaging with our research. We appreciate the opportunity to address your queries and provide further clarification on our findings. Regarding the discrepancy between our study and the previous findings by Regolo et al. on the predictive value of NLR for mortality in COVID-19 patients, we acknowledge the importance of understanding the nuances in different patient cohorts and disease presentations. While our study highlights the association of NLR and PLR with hospitalization duration and recovery based on the patient's age, the findings interestingly showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. In our study, univariate analysis showed that admission NLR levels significantly differed between survivors and non-survivors [5.8 (0.9-53.0) vs. 3.2 (0.27-72.0); p=0.001]. Consistent with Regolo et al., our data showed an association of NLR with predicting mortality and delayed recovery in COVID-19 patients. The crude odds ratio for NLR was 1.078 (95% CI 1.049-1.109; p=0.001) for mortality and 1.034 (95% CI 0.996-1.072; p=0.078) for delayed recovery. However, the independent association of NLR (OR 1.039; 95% CI 0.998-1.082; p=0.051) with mortality relatively disappeared in our multivariate model after adjusting for age and other relevant covariates. Though our data suggest a clinically relevant OR of 1.039, it failed to reach statistical significance (p=0.051). In the context of multivariate analysis, collinearity with other variables can diminish the independent predictive power of NLR. This may be due to the fact that NLR, PLR, and CRP, to some extent, are associated with the same pathophysiological chain, i.e., inflammation. Their interaction means that CRP might account for most of the variance related to inflammation, reducing the unique contribution of NLR. Similarly, PLR is another marker of inflammation and immune response, and its inclusion alongside NLR can lead to redundancy, with one ratio (PLR or NLR) not adding significant predictive value over the other. Additionally, larger sample sizes in multivariate analysis can increase the model's complexity, requiring careful consideration of potential confounders and interactions between variables. The specific observed differences between our study and Regolo et al.'s findings could be attributed to various factors, including differences in patient demographics, such as age, disease severity, methodology, and sample size. Our study focuses on a specific cohort of young patients during the initial phase of the pandemic, which may have unique characteristics compared to the older patient populations studied by Regolo et al. (the median age of the patient population was 72). Moreover, our cohort sample size was 1016 compared to 411 in Regolo et al.'s study. As sample sizes increase, the effect sizes of variables may become smaller, and statistically significant associations may be observed with smaller magnitudes of effect. This phenomenon can sometimes lead to a perception that the associations are less clinically meaningful. Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world. The overall in-hospital mortality was 11.9%, lower than the 19.5% reported by Regolo et al. Lower mortality was also observed in the elderly population (aged >55). Lastly, we acknowledge the lack of data on respiratory function in our study. We believe that the objectives of our study were clearly defined and focused. Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work. Reviewer 1 Query: This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome. In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so it makes it difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid19. Reply to reviewer-1 I would like to thank the reviewer for their thoughtful comments and for engaging with our research. We appreciate the opportunity to address your queries and provide further clarification on our findings. Regarding the discrepancy between our study and the previous findings by Regolo et al. on the predictive value of NLR for mortality in COVID-19 patients, we acknowledge the importance of understanding the nuances in different patient cohorts and disease presentations. While our study highlights the association of NLR and PLR with hospitalization duration and recovery based on the patient's age, the findings interestingly showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. In our study, univariate analysis showed that admission NLR levels significantly differed between survivors and non-survivors [5.8 (0.9-53.0) vs. 3.2 (0.27-72.0); p=0.001]. Consistent with Regolo et al., our data showed an association of NLR with predicting mortality and delayed recovery in COVID-19 patients. The crude odds ratio for NLR was 1.078 (95% CI 1.049-1.109; p=0.001) for mortality and 1.034 (95% CI 0.996-1.072; p=0.078) for delayed recovery. However, the independent association of NLR (OR 1.039; 95% CI 0.998-1.082; p=0.051) with mortality relatively disappeared in our multivariate model after adjusting for age and other relevant covariates. Though our data suggest a clinically relevant OR of 1.039, it failed to reach statistical significance (p=0.051). In the context of multivariate analysis, collinearity with other variables can diminish the independent predictive power of NLR. This may be due to the fact that NLR, PLR, and CRP, to some extent, are associated with the same pathophysiological chain, i.e., inflammation. Their interaction means that CRP might account for most of the variance related to inflammation, reducing the unique contribution of NLR. Similarly, PLR is another marker of inflammation and immune response, and its inclusion alongside NLR can lead to redundancy, with one ratio (PLR or NLR) not adding significant predictive value over the other. Additionally, larger sample sizes in multivariate analysis can increase the model's complexity, requiring careful consideration of potential confounders and interactions between variables. The specific observed differences between our study and Regolo et al.'s findings could be attributed to various factors, including differences in patient demographics, such as age, disease severity, methodology, and sample size. Our study focuses on a specific cohort of young patients during the initial phase of the pandemic, which may have unique characteristics compared to the older patient populations studied by Regolo et al. (the median age of the patient population was 72). Moreover, our cohort sample size was 1016 compared to 411 in Regolo et al.'s study. As sample sizes increase, the effect sizes of variables may become smaller, and statistically significant associations may be observed with smaller magnitudes of effect. This phenomenon can sometimes lead to a perception that the associations are less clinically meaningful. Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world. The overall in-hospital mortality was 11.9%, lower than the 19.5% reported by Regolo et al. Lower mortality was also observed in the elderly population (aged >55). Lastly, we acknowledge the lack of data on respiratory function in our study. We believe that the objectives of our study were clearly defined and focused. Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 03 May 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 07 Jul 25 read read Version 1 03 May 24 read read Ivan Isaia , Clinica Medica-Ospedale Cannizzaro, Catania, Italy; University of Catania, Catania, Italy Lorenzo Malatino , University of Catania, Catania, Italy Roberto Paganelli , UniCamillus, Rome, Italy Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Malatino L et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Nov 2025 | for Version 2 Ivan Isaia , Department of Clinical and Experimental Medicine, University of Catania, Clinica Medica-Ospedale Cannizzaro, Catania, Italy; Clinical and experimental medicine, University of Catania, Catania, Catania, Italy Lorenzo Malatino , Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy 0 Views copyright © 2025 Malatino L et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I revised the second version of this article. Two references I suggested to include in the reference list are not yet acknowledged: 1) Cataudella E. et al., 2017: 2) Regolo M. et al., J Clin Med 2023. Both references support my comments included in my first report and specifically regard the clinical frame within this paper by El-Menyar actually falls. Furhermore, I strongly disagree, by the way, with the assumption concluding Authors' rebuttal \"Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work\". In fact, papers addressed to. clinical audience should always focus in nature on their impact on the pathooenetic chain of the disease. state they are related to.. Competing Interests No competing interests were disclosed. Reviewer Expertise Internal and Clinical Medicine, Cardiology, Neutrophil-to-Lymphocyte Ratio, Infectious diseases, Covid-19 disease. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (0) Isaia I and Malatino L. Peer Review Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.184107.r397253) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-446/v2#referee-response-397253 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Paganelli R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Jul 2025 | for Version 2 Roberto Paganelli , International Medical University in Rome, UniCamillus, Rome, Italy 0 Views copyright © 2025 Paganelli R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The article is acceptable now. I recommend Indexing. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 05 Nov 2025 Ayman El-Menyar, Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Qatar Thank you. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Paganelli R. Peer Review Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.184107.r397254) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-446/v2#referee-response-397254 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Paganelli R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Jun 2025 | for Version 1 Roberto Paganelli , International Medical University in Rome, UniCamillus, Rome, Italy 0 Views copyright © 2025 Paganelli R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is a retrospective study of COVID-19 patients' outcome at a group of Hospitals in Qatar in 2020, at the beginning of the pandemic. The authors seeked to evaluate the association of NLR and PLR at admission with mortality and severity of patients. The results show that NLR predicts mortality in all cases, and PLR is better for length of hospital stay. These parameters however are non independent predictors, and older subjects had a worse outcome. Most of this was known since the beginning of the pandemic, so it adds very little to existing literature. Moreover, the study does not mention the ethnic origin of the patients, who were mostly males. It may deserve publication as a record of the pandemic viewed from a small country with large immigration. The English language needs improvement. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise immunology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Paganelli R. Peer Review Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.160935.r385519) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-446/v1#referee-response-385519 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Malatino L et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Jun 2024 | for Version 1 Lorenzo Malatino , Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy Ivan Isaia , Department of Clinical and Experimental Medicine, University of Catania, Clinica Medica-Ospedale Cannizzaro, Catania, Italy; Clinical and experimental medicine, University of Catania, Catania, Catania, Italy 0 Views copyright © 2024 Malatino L et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome. In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so making difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid-19. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Regolo M, Vaccaro M, Sorce A, Stancanelli B, et al.: Neutrophil-to-Lymphocyte Ratio (NLR) Is a Promising Predictor of Mortality and Admission to Intensive Care Unit of COVID-19 Patients. J Clin Med . 2022; 11 (8). PubMed Abstract | Publisher Full Text 2. Cataudella E, Giraffa CM, Di Marca S, Pulvirenti A, et al.: Neutrophil-To-Lymphocyte Ratio: An Emerging Marker Predicting Prognosis in Elderly Adults with Community-Acquired Pneumonia. J Am Geriatr Soc . 2017; 65 (8): 1796-1801 PubMed Abstract | Publisher Full Text 3. Buonacera A, Stancanelli B, Colaci M, Malatino L: Neutrophil to Lymphocyte Ratio: An Emerging Marker of the Relationships between the Immune System and Diseases. Int J Mol Sci . 2022; 23 (7). PubMed Abstract | Publisher Full Text 4. Regolo M, Sorce A, Vaccaro M, Colaci M, et al.: Assessing Humoral Immuno-Inflammatory Pathways Associated with Respiratory Failure in COVID-19 Patients. J Clin Med . 2023; 12 (12). PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Internal and Clinical Medicine, Cardiology, Neutrophil-to-Lymphocyte Ratio We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 07 Jul 2025 Ayman El-Menyar, Clinical Research, Trauma & Vascular Surgery Section, Hamad Medical Corporation, Doha, Qatar Reviewer 1 Query: This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome. In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so it makes it difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid19. Reply to reviewer-1 I would like to thank the reviewer for their thoughtful comments and for engaging with our research. We appreciate the opportunity to address your queries and provide further clarification on our findings. Regarding the discrepancy between our study and the previous findings by Regolo et al. on the predictive value of NLR for mortality in COVID-19 patients, we acknowledge the importance of understanding the nuances in different patient cohorts and disease presentations. While our study highlights the association of NLR and PLR with hospitalization duration and recovery based on the patient's age, the findings interestingly showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. In our study, univariate analysis showed that admission NLR levels significantly differed between survivors and non-survivors [5.8 (0.9-53.0) vs. 3.2 (0.27-72.0); p=0.001]. Consistent with Regolo et al., our data showed an association of NLR with predicting mortality and delayed recovery in COVID-19 patients. The crude odds ratio for NLR was 1.078 (95% CI 1.049-1.109; p=0.001) for mortality and 1.034 (95% CI 0.996-1.072; p=0.078) for delayed recovery. However, the independent association of NLR (OR 1.039; 95% CI 0.998-1.082; p=0.051) with mortality relatively disappeared in our multivariate model after adjusting for age and other relevant covariates. Though our data suggest a clinically relevant OR of 1.039, it failed to reach statistical significance (p=0.051). In the context of multivariate analysis, collinearity with other variables can diminish the independent predictive power of NLR. This may be due to the fact that NLR, PLR, and CRP, to some extent, are associated with the same pathophysiological chain, i.e., inflammation. Their interaction means that CRP might account for most of the variance related to inflammation, reducing the unique contribution of NLR. Similarly, PLR is another marker of inflammation and immune response, and its inclusion alongside NLR can lead to redundancy, with one ratio (PLR or NLR) not adding significant predictive value over the other. Additionally, larger sample sizes in multivariate analysis can increase the model's complexity, requiring careful consideration of potential confounders and interactions between variables. The specific observed differences between our study and Regolo et al.'s findings could be attributed to various factors, including differences in patient demographics, such as age, disease severity, methodology, and sample size. Our study focuses on a specific cohort of young patients during the initial phase of the pandemic, which may have unique characteristics compared to the older patient populations studied by Regolo et al. (the median age of the patient population was 72). Moreover, our cohort sample size was 1016 compared to 411 in Regolo et al.'s study. As sample sizes increase, the effect sizes of variables may become smaller, and statistically significant associations may be observed with smaller magnitudes of effect. This phenomenon can sometimes lead to a perception that the associations are less clinically meaningful. Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world. The overall in-hospital mortality was 11.9%, lower than the 19.5% reported by Regolo et al. Lower mortality was also observed in the elderly population (aged >55). Lastly, we acknowledge the lack of data on respiratory function in our study. We believe that the objectives of our study were clearly defined and focused. Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Malatino L and Isaia I. Peer Review Report For: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study [version 2; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 13 :446 ( https://doi.org/10.5256/f1000research.160935.r282772) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-446/v1#referee-response-282772 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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