COVID-19 disease—Temporal analyses of complete blood count parameters over course of illness, and relationship to patient demographics and management outcomes in survivors and non-survivors: A longitudinal descriptive cohort study

Simone Lanini; Chiara Montaldo; Emanuele Nicastri; Francesco Vairo; Chiara Agrati; Nicola Petrosillo; Paola Scognamiglio; Andrea Antinori; Vincenzo Puro; Antonino Di Caro; Gabriella De Carli; Assunta Navarra; Alessandro Agresta; Claudia Cimaglia; Fabrizio Palmieri; Gianpiero D’Offizi; Luisa Marchioni; Gary Pignac Kobinger; Markus Maeurer; Enrico Girardi; Maria Rosaria Capobianchi; Alimuddin Zumla; Franco Locatelli; Giuseppe Ippolito

doi:10.1371/journal.pone.0244129

. 2020 Dec 28;15(12):e0244129. doi: 10.1371/journal.pone.0244129

COVID-19 disease—Temporal analyses of complete blood count parameters over course of illness, and relationship to patient demographics and management outcomes in survivors and non-survivors: A longitudinal descriptive cohort study

Simone Lanini ^1,^*,^#, Chiara Montaldo ^1,^#, Emanuele Nicastri ¹, Francesco Vairo ¹, Chiara Agrati ¹, Nicola Petrosillo ¹, Paola Scognamiglio ¹, Andrea Antinori ¹, Vincenzo Puro ¹, Antonino Di Caro ¹, Gabriella De Carli ¹, Assunta Navarra ¹, Alessandro Agresta ¹, Claudia Cimaglia ¹, Fabrizio Palmieri ¹, Gianpiero D’Offizi ¹, Luisa Marchioni ¹, Gary Pignac Kobinger ², Markus Maeurer ^3,⁴, Enrico Girardi ¹, Maria Rosaria Capobianchi ¹, Alimuddin Zumla ^5,^6,^#, Franco Locatelli ^7,^#, Giuseppe Ippolito ^1,^#

Editor: Silvia Ricci⁸

¹National Institute for Infectious Diseases, Lazzaro Spallanzani, IRCCS, Rome, Italy

²Centre de Recherche en Infectiologie de l'Université Laval, Quebec City, QC, Canada

³Champalimaud Centre for the Unknown, Lisbon, Portugal

⁴University of Mainz, Mainz, Germany

⁵Department of Infection, Division of Infection and Immunity, University College London, London, United Kingdom

⁶NHS Foundation Trust, London, United Kingdom

⁷Department of Pediatric Hematology and Oncology IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy

⁸Meyer Children’s University Hospital - University of Florence, ITALY

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: simone.lanini@inmi.it

Contributed equally.

Roles

Simone Lanini: Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Chiara Montaldo: Conceptualization, Formal analysis, Validation, Writing – original draft, Writing – review & editing

Emanuele Nicastri: Conceptualization, Data curation, Investigation, Methodology, Writing – review & editing

Francesco Vairo: Data curation, Investigation, Methodology, Validation, Writing – review & editing

Chiara Agrati: Conceptualization, Investigation, Validation, Writing – review & editing

Nicola Petrosillo: Conceptualization, Data curation, Validation, Writing – original draft

Paola Scognamiglio: Data curation, Investigation, Validation, Writing – review & editing

Andrea Antinori: Conceptualization, Data curation, Investigation, Validation, Writing – review & editing

Vincenzo Puro: Conceptualization, Data curation, Investigation, Methodology, Validation, Writing – review & editing

Antonino Di Caro: Conceptualization, Data curation, Methodology, Resources, Validation, Writing – review & editing

Gabriella De Carli: Data curation, Investigation, Visualization, Writing – review & editing

Assunta Navarra: Data curation, Formal analysis, Software, Validation, Writing – review & editing

Alessandro Agresta: Data curation, Formal analysis, Software, Validation, Writing – review & editing

Claudia Cimaglia: Data curation, Formal analysis, Software, Validation, Writing – review & editing

Fabrizio Palmieri: Conceptualization, Data curation, Validation, Writing – review & editing

Gianpiero D’Offizi: Conceptualization, Data curation, Validation, Writing – review & editing

Luisa Marchioni: Conceptualization, Data curation, Validation, Writing – review & editing

Gary Pignac Kobinger: Conceptualization, Investigation, Methodology, Writing – review & editing

Markus Maeurer: Conceptualization, Investigation, Methodology, Validation, Writing – review & editing

Enrico Girardi: Conceptualization, Data curation, Formal analysis, Methodology, Writing – review & editing

Maria Rosaria Capobianchi: Conceptualization, Data curation, Methodology, Validation, Writing – review & editing

Alimuddin Zumla: Conceptualization, Data curation, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

Franco Locatelli: Conceptualization, Investigation, Methodology, Validation, Visualization, Writing – review & editing

Giuseppe Ippolito: Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing – review & editing

Silvia Ricci: Editor

PMCID: PMC7769441 PMID: 33370366

Abstract

Background

Detailed temporal analyses of complete (full) blood count (CBC) parameters, their evolution and relationship to patient age, gender, co-morbidities and management outcomes in survivors and non-survivors with COVID-19 disease, could identify prognostic clinical biomarkers.

Methods

From 29 January 2020 until 28 March 2020, we performed a longitudinal cohort study of COVID-19 inpatients at the Italian National Institute for Infectious Diseases, Rome, Italy. 9 CBC parameters were studied as continuous variables [neutrophils, lymphocytes, monocytes, platelets, mean platelet volume, red blood cell count, haemoglobin concentration, mean red blood cell volume and red blood cell distribution width (RDW %)]. Model-based punctual estimates, as average of all patients’ values, and differences between survivors and non-survivors, overall, and by co-morbidities, at specific times after symptoms, with relative 95% CI and P-values, were obtained by marginal prediction and ANOVA- style joint tests. All analyses were carried out by STATA 15 statistical package.

Main findings

379 COVID-19 patients [273 (72% were male; mean age was 61.67 (SD 15.60)] were enrolled and 1,805 measures per parameter were analysed. Neutrophils’ counts were on average significantly higher in non-survivors than in survivors (P<0.001) and lymphocytes were on average higher in survivors (P<0.001). These differences were time dependent. Average platelets’ counts (P<0.001) and median platelets’ volume (P<0.001) were significantly different in survivors and non-survivors. The differences were time dependent and consistent with acute inflammation followed either by recovery or by death. Anaemia with anisocytosis was observed in the later phase of COVID-19 disease in non-survivors only. Mortality was significantly higher in patients with diabetes (OR = 3.28; 95%CI 1.51–7.13; p = 0.005), obesity (OR = 3.89; 95%CI 1.51–10.04; p = 0.010), chronic renal failure (OR = 9.23; 95%CI 3.49–24.36; p = 0.001), COPD (OR = 2.47; 95% IC 1.13–5.43; p = 0.033), cardiovascular diseases (OR = 4.46; 95%CI 2.25–8.86; p = 0.001), and those >60 years (OR = 4.21; 95%CI 1.82–9.77; p = 0.001). Age (OR = 2.59; 95%CI 1.04–6.45; p = 0.042), obesity (OR = 5.13; 95%CI 1.81–14.50; p = 0.002), renal chronic failure (OR = 5.20; 95%CI 1.80–14.97; p = 0.002) and cardiovascular diseases (OR 2.79; 95%CI 1.29–6.03; p = 0.009) were independently associated with poor clinical outcome at 30 days after symptoms’ onset.

Interpretation

Increased neutrophil counts, reduced lymphocyte counts, increased median platelet volume and anaemia with anisocytosis, are poor prognostic indicators for COVID19, after adjusting for the confounding effect of obesity, chronic renal failure, COPD, cardiovascular diseases and age >60 years.

Background

Since the first report of SARS-CoV-2 as a novel human zoonotic pathogen in late December, 2019 [1], from Wuhan (China), the Coronavirus disease 2019 (COVID-19) pandemic [2] has rapidly spread worldwide. As of June 21^st 2020, there have been 8,525,042 cases of COVID-19 with 456,973 deaths reported to the WHO [3]. Whilst COVID-19 predominantly affects the respiratory system, it is a multisystem disease, with a wide spectrum of clinical presentations from asymptomatic, mild and moderate, to severe, fulminant disease. The elderly and those with underlying co-morbidities such as cardiovascular disease, diabetes, chronic respiratory diseases and cancer appear to have increased risk of death [4]. As underlined by a recent review, COVID‐19 has a significant impact on the hematopoietic system: lymphopenia, neutrophil/lymphocyte ratio and peak platelet/lymphocyte ratio may be considered as cardinal laboratory findings, with prognostic potential [5]. Complete blood count (CBC) is a routine investigation for all inpatients and provides vital parameters which can inform clinical management, including diagnosis, presence of infection or inflammation, anaemia, response to treatment, pathogenesis and stage of an inflammatory process. A study conducted in five different countries in Asia, Africa and United States shows that CBC is the most commonly used initial laboratory test [6]. A retrospective study from Singapore concluded that clinical findings and basic blood tests may be useful in identifying individuals with a higher probability of having COVID-19 [7].

To determine the temporal evolution of CBC parameters over the course of COVID-19 illness in survivors and non-survivors, and their potential association with patient clinical management outcomes, we performed an in depth analysis of routinely collected clinical and laboratory data of inpatients with COVID-19 disease admitted to the largest Italian COVID-19 clinical centre since the beginning of the Italian outbreak.

Methods

Study design

We conducted a longitudinal cohort study of all consecutive patients with laboratory confirmed COVID-19 admitted at the Italian National Institute for Infectious Diseases “Lazzaro Spallanzani” (INMI), in Rome (Italy) since the first Italian cases identified in Rome on January 29^th, 2020.

Setting

INMI is the largest Italian hospital for infectious diseases. Since the start of COVID-19 epidemic, INMI has been coordinating the clinical network for COVID-19 care in Lazio, an administrative Region of Italy with about 6 million inhabitants, whose main city is Rome. As regional referral centre, INMI is endowed with state-of-art laboratory and clinical facilities to care for COVID-19 patients at any stage of the disease. Thus, INMI may receive patients from other clinical centres but discharges them only after clinically recovery or death. Moreover, INMI runs the Regional Service for Surveillance of Infectious Diseases (SERESMI), which daily receives the notifications of all the new confirmed COVID-19 cases reported throughout the Region.

Ethics/IRB approval

This study was approved by the IRB of INMI, in Rome (Italy).

Patients and follow-up

All patients with confirmed COVID-19 admitted to INMI between the 29 January [8] and 28 March 2020 were followed up until death or discharge from hospital. Patients’ demographics, clinical features and laboratory tests results were collected on standardised forms.

Variables studied

We analysed nine CBC parameters (as continuous variables) including: neutrophils’ count (cells/mm³), lymphocytes’ count (cells/mm³), monocytes’ count (cells/mm³), platelets’ count (cells/mm³), mean platelet volume (MPV; fL), red blood cells’ count (RBC; cells/mm³), haemoglobin concentration (Hb; g/dL), mean red blood cell volume (MCV; fL) and red blood cell distribution width (RDW %). Values of all these parameters are recorded for 21 days since symptoms onset.

Other variables (covariates) included: patients life status at 30 days after symptoms’ onset (binary, either survivors or non-survivors); time since symptoms’ onset (as a continuous variable in day); age (binary; <60 or older); sex (binary); obesity (binary, body max index >30 or lower); chronic renal failure; cardiovascular disease (including hypertension); diabetes; history of cancer throughout life and chronic obstructive pulmonary disease (COPD).

Definitions

Survivors were defined as patients who recovered and were discharged from hospital or who were still hospitalized within 30 days after symptoms onset.

Non-survivors are all those subjects who died within 30 days after onset of symptoms.

Data collection and data quality assessment

All patients’ demographic, laboratory and clinical data (CBC parameter values, day of hospital admission, day of patients’ discharge and patient’s life status at discharge) were collected in the Regional Service for the Epidemiology and surveillance of Infectious Diseases database.

Laboratory methods

SARS-CoV-2 RNA was extracted by QIAsymphony (QIAgen, Germany) and Real-time reverse-transcription PCR (RT-PCR) targeting E and RdRp viral genes was used to assess the presence of SARS-CoV-2 RNA, as per Corman protocol [9].

CBC was performed using the fully automated haematology analyser Sismex XN 1500.

Statistics and modelling

All data analyses were carried out by STATA 15 statistical package (S1 File).

The temporal kinetics of the nine blood counts parameters were modelled according to separate mixed effect multivariable linear-log regression models (MEMLR) [10,11]. Overall, we modelled a total of 1,805 measures per parameter taken from the 379 subjects in follow-up. The average number of measures per patients was 4.8 (range 1–22) and the average number of observation per day was 82.0 (range 9–122)). For each one of the nine analysed CBC parameters, we provided a plot for describing parameter variation over time (kinetic), either for survivors or for non-survivors. The random component of each model included a random intercept at patient’s level, a random slope at the time since symptoms’ onset and an unstructured covariance matrix for dealing with measures imbalance over time (i.e. unequal number of observation per patient). The fixed component included one dependent variable (i.e. one of nine blood counts parameters in natural log-form), two main independent variables for modelling kinetics (i.e. patient’s clinical outcome and time since symptoms’ onset) and a set of potential confounders (i.e. the set of variables with a significant association with clinical outcome in previous MVLR model).

The kinetics of each CBC parameter was modelled assuming time since symptoms’ onset as either linear or quadratic component. Thus, we explored four different hypothetical shapes of associations (i.e. liner positive, linear negative, U-shaped and inverse U-shaped trajectory). Linear association was preferred over quadratic association whenever LRT was <0.100 (assuming linear MEMLR nested into quadratic MEMLR). A full interaction term between time since symptoms’ onset and clinical outcome was used so that the kinetics of each CBC parameter could have a different temporal trend either in survivors or in non-survivors.

We obtained model-based punctual estimates of CBC parameters at different times and difference between survivors and non-survivors as overall and at specific time after symptoms’ onset with relative 95% CI and P-values, by marginal prediction and ANOVA- style joint tests. Estimate values of CBC parameters and relative difference were always shown in exponential form for simplicity. Multivariable logistic regression (MVLR) models were carried out for assessing the association between clinical outcome and patients’ demographics. All kinetic models were adjusted for the four potential confounders selected in the MVLR (i.e. age, obesity, chronic renal failure and cardiovascular disease).

Results

Patients recruited and demographics

Fig 1 shows study patients’ flowchart. Four-hundred-ten patients were admitted with a diagnosis of COVID-19 at INMI Spallanzani between January 29 and March 28. Of them, 379 patients were included in the analyses. Thirty patients (7.32%) were excluded due to incomplete records and one died before CBC was performed. Forty-one patients died within 30 days after symptoms’ onset with a case fatality rate of 10.82%. Among the 379 patients included in the analysis, 273 (72.03%) were male and the mean age was 61.67 (SD 15.60).

Patients’ demographics and co-morbidities are shown in Table 1.

Table 1. Patients’ demographics and co-morbidities.

Patients feature		Descriptive analysis						Bivaribale analysis				Multivariable analysis
		Total		survivors		Non-survivors		OR	95% CI		p-value	OR	95% CI		p-value
		N	%	N	%	N	%	OR	95% CI		p-value	OR	95% CI		p-value
Age	<60 years	164	43.27%	157	95.73%	7	4.27%	4.21	1.82	9.77	0.001	2.59	1.04	6.45	0.042
Age	>60 years	215	56.73%	181	84.19%	34	15.81%	4.21	1.82	9.77	0.001	2.59	1.04	6.45	0.042
Sex	female	106	27.97%	93	87.74%	13	12.26%	0.82	0.41	1.65	0.577	-	-	-	-
Sex	male	273	72.03%	245	89.74%	28	10.26%	0.82	0.41	1.65	0.577	-	-	-	-
Diabetes	no	334	88.13%	304	91.02%	30	8.98%	3.28	1.51	7.13	0.005	-	-	-	-
Diabetes	yes	45	11.87%	34	75.56%	11	24.44%	3.28	1.51	7.13	0.005	-	-	-	-
Neoplasm	no	360	94.99%	322	89.44%	38	10.56%	1.59	0.44	5.70	0.498	-	-	-	-
Neoplasm	yes	19	5.01%	16	84.21%	3	15.79%	1.59	0.44	5.70	0.498	-	-	-	-
Obesity	no	355	93.67%	321	90.42%	34	9.58%	3.89	1.51	10.04	0.010	5.13	1.81	14.50	0.002
Obesity	yes	24	6.33%	17	70.83%	7	29.17%	3.89	1.51	10.04	0.010	5.13	1.81	14.50	0.002
Chronic renal failure	no	360	94.99%	328	91.11%	32	8.89%	9.23	3.49	24.36	0.001	5.20	1.80	14.97	0.002
Chronic renal failure	yes	19	5.01%	10	52.63%	9	47.37%	9.23	3.49	24.36	0.001	5.20	1.80	14.97	0.002
COPD	no	330	87.07%	299	90.61%	31	9.39%	2.47	1.13	5.43	0.033	-	-	-	-
COPD	yes	49	12.93%	39	79.59%	10	20.41%	2.47	1.13	5.43	0.033	-	-	-	-
Cardiovascular disease	no	250	65.96%	236	94.40%	4	1.60%	4.46	2.25	8.86	0.001	2.79	1.29	6.03	0.009
Cardiovascular disease	yes	129	34.04%	102	79.07%	27	20.93%	4.46	2.25	8.86	0.001	2.79	1.29	6.03	0.009
Overall	-	379	100.00%	338	89.18%	41	10.82%	-	-	-	-	-	-	-	-

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Association between patients baseline conditions and clinical outcome

Bivariable analyses showed that mortality was significantly higher in patients aged 60 years or older (OR = 4.21; 95%CI 1.82–9.77; p = 0.001), in diabetic patients (OR = 3.28; 95%CI 1.51–7.13; p = 0.005), in obese patients (OR = 3.89; 95%CI 1.51–10.04; p = 0.010), in patients with underlying chronic renal failure (OR = 9.23; 95%CI 3.49–24.36; p = 0.001), in patients with COPD (OR = 2.47; 95% IC 1.13–5.43; p = 0.033) and in patients with cardiovascular diseases (OR = 4.46; 95%CI 2.25–8.86; p = 0.001). No association was found between clinical outcome and sex, and between clinical outcome and history of neoplasm (defined as any anamnestic data of oncological disease).

The multivariable analysis showed that only age (OR = 2.59; 95%CI 1.04–6.45; p = 0.042), obesity (OR = 5.13; 95%CI 1.81–14.50; p = 0.002), renal chronic failure (OR = 5.20; 95%CI 1.80–14.97; p = 0.002) and cardiovascular diseases (OR 2.79; 95%CI 1.29–6.03; p = 0.009) were independently associated with patient clinical outcome at 30 day after symptoms onset. Table 1 reports the results of bivariable and multivariable analysis. Additional S2 File reports the details of the model building and variable selection process.

Leukocytes parameters kinetics

Three leukocyte parameters, neutrophils’ counts, lymphocytes’ counts and monocytes’ counts, were analysed (Fig 2 and Table 2). Average neutrophils’ counts (Fig 2A and 2B) were significantly higher in non-survivors than in survivors (p<0.001). The temporal analysis suggested that, at the time of symptoms’ onset, survivors and non-survivors had similar level of neutrophil counts (p = 0.191). In survivors, neutrophils’ counts remained steadily and within the normal range throughout the follow-up. In contrast, in non-survivors, neutrophils’ counts sharply increased, being significantly different from those of survivors by day 6 after symptoms’ onset. Moreover, the model predicted that by day 13, the average neutrophils’ counts in non-survivors steadily exceeded the upper normal limit range (8,000 cells/mm³). Among the considered confounders, only age was significantly associated to higher neutrophil counts (p = 0.027), while no significant association was found for obesity, chronic renal failure and cardiovascular diseases.

Table 2. Temporal evolution of the differences of neutrophils’, lymphocyte’s and monocytes’ counts, between survivors and non-survivors (diff).

	Neutrophils’ counts (cells/mm3)				Lymphocytes’ counts (cells/mm3)				Monocytes’ counts (cells/mm3)
Day	Diff	llb	ulb	p	Diff	llb	ulb	p	diff	llb	ulb	p
0	841.22	-419.16	2101.60	0.191	-330.16	-633.48	-26.85	0.033	-66.94	-183.12	49.25	0.259
1	821.01	-351.81	1993.82	0.170	-332.25	-578.06	-86.44	0.008	-70.75	-170.66	29.15	0.165
2	816.62	-281.34	1914.58	0.145	-333.86	-536.93	-130.80	0.001	-74.18	-160.95	12.60	0.094
3	831.14	-207.17	1869.46	0.117	-335.53	-507.59	-163.47	<0.001	-77.30	-153.77	-0.82	0.048
4	868.00	-128.49	1864.49	0.088	-337.66	-488.02	-187.30	<0.001	-80.18	-148.90	-11.47	0.022
5	930.94	-43.81	1905.69	0.061	-340.60	-476.49	-204.70	<0.001	-82.89	-146.11	-19.67	0.010
6	1024.11	49.55	1998.67	0.039	-344.61	-471.51	-217.72	<0.001	-85.46	-145.14	-25.78	0.005
7	1152.04	155.75	2148.32	0.023	-349.96	-471.86	-228.07	<0.001	-87.93	-145.69	-30.18	0.003
8	1319.68	280.33	2359.03	0.013	-356.89	-476.64	-237.15	<0.001	-90.33	-147.44	-33.22	0.002
9	1532.47	429.70	2635.23	0.007	-365.64	-485.28	-245.99	<0.001	-92.67	-150.10	-35.24	0.002
10	1796.35	610.43	2982.26	0.003	-376.46	-497.56	-255.37	<0.001	-94.96	-153.44	-36.48	0.002
11	2117.84	828.49	3407.20	0.001	-389.65	-513.48	-265.83	<0.001	-97.20	-157.30	-37.09	0.002
12	2504.12	1088.64	3919.61	0.001	-405.53	-533.30	-277.76	<0.001	-99.37	-161.61	-37.12	0.002
13	2963.08	1393.91	4532.26	<0.001	-424.48	-557.51	-291.45	<0.001	-101.44	-166.42	-36.46	0.002
14	3503.46	1745.19	5261.74	<0.001	-446.96	-586.79	-307.13	<0.001	-103.38	-171.89	-34.86	0.003
15	4134.97	2140.90	6129.04	<0.001	-473.49	-622.04	-324.94	<0.001	-105.12	-178.34	-31.90	0.005
16	4868.41	2576.86	7159.96	<0.001	-504.73	-664.45	-345.02	<0.001	-106.59	-186.26	-26.92	0.009
17	5715.91	3046.30	8385.52	<0.001	-541.46	-715.48	-367.44	<0.001	-107.67	-196.30	-19.04	0.017
18	6691.13	3539.92	9842.34	<0.001	-584.63	-776.99	-392.28	<0.001	-108.22	-209.31	-7.13	0.036
19	7809.51	4045.91	11573.11	<0.001	-635.40	-851.24	-419.56	<0.001	-108.04	-226.24	10.16	0.073
20	9088.61	4549.64	13627.59	<0.001	-695.19	-941.08	-449.29	<0.001	-106.90	-248.20	34.40	0.138
21	10548.49	5033.08	16063.90	<0.001	-765.73	-1050.04	-481.43	<0.001	-104.47	-276.42	67.49	0.234
Joint				<0.001				<0.001				0.058

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llb = low limit bound, ulb = lower limit bound.

Average lymphocytes’ counts (Fig 2C and 2D) were significantly lower in non-survivors than in survivors (p-joint <0.001) since the first day of symptoms’ onset. The model predicted that lymphocytes’ counts were significantly lower in non-survivors than in survivors since the first day after symptoms’ onset (p = 0.033). In particular, the difference of lymphocytes’ counts between survivors and non-survivors widened over time, being estimated at 330.1 (95% CI 633.48–26.85; p = 0.033) and 765.73 (95% CI 1050.04–481.43; p<0.001) cells per mm³ at symptoms’ onset and at the end of follow-up, respectively. Moreover, average lymphocytes’ count was always below 1,000 cells per mm³ in non-survivors. In contrast, the level of lymphocytes normalized by the day 15 in survivors. Among the considered confounders, age and chronic renal failure were significantly associated to lower lymphocyte counts (respectively p<0.001, and p = 0.052) while no significant association was found for obesity and cardiovascular diseases.

Average monocytes’ counts (Fig 2E and 2F) were marginally lower in non-survivors than in survivors (p-joint <0.058). The temporal analysis suggested that average variation of monocytes was within the normal range in both survivors and non-survivors. Among the considered confounders, only age was significantly associated to lower monocytes’ counts (p = 0.038) while no significant association was found for obesity, chronic renal failure and cardiovascular diseases.

Red blood cells parameters and kinetics

Four RBC parameters (i.e. RBC count, Hb concentration, MCV, RDW) were analysed (Fig 3 and Table 3). Average RBC (Fig 3A and 3B) were significantly lower in non-survivors than in survivors (p-joint <0.001). The temporal analysis suggested that, at the time of symptoms’ onset, survivors and non-survivors had similar RBC counts (p = 0.257). The model predicted that average level of RBC became significantly different between survivors and non-survivors by day 2 after symptoms’ onset (p = 0.033). Average RBC in non-survivors steadily decreased over time being below to normal value (3.8 million cell/mm³) by day 7 after symptoms onset. In contrast, we found no evidence for a significant decrease of average RBC counts below normal level in survivors. Among the considered confounders, age (p<0.001) and chronic renal failure (p<0.001) were significantly associated to lower RBC counts. No significant association was found for obesity and cardiovascular diseases.

Table 3. Temporal evolution of the difference of red cell count, Haemoglobin level, MCV and RDW between survivors and non-survivors (diff).

	RBC counts (cells/mm3)				Hb (g/dL)				MCV (fL)				RDW (%)
Day	diff	llb	ulb	p	diff	llb	ulb	p	diff	llb	ulb	p	diff	llb	ulb	p
0	-186987.30	-510433.40	136458.70	0.257	-0.68	-1.40	0.04	0.063	2.34	-0.15	4.83	0.066	0.78	0.25	1.31	0.004
1	-238952.20	-529112.80	51208.26	0.107	-0.72	-1.41	-0.03	0.041	2.74	0.36	5.12	0.024	0.76	0.26	1.26	0.003
2	-286012.50	-548989.80	-23035.31	0.033	-0.75	-1.41	-0.09	0.025	3.10	0.81	5.39	0.008	0.74	0.27	1.22	0.002
3	-328412.70	-569877.30	-86948.04	0.008	-0.79	-1.42	-0.16	0.015	3.42	1.20	5.64	0.003	0.74	0.28	1.20	0.002
4	-366377.20	-591452.70	-141301.60	0.001	-0.82	-1.43	-0.21	0.008	3.70	1.53	5.87	0.001	0.75	0.30	1.20	0.001
5	-400112.00	-613254.00	-186970.10	<0.001	-0.85	-1.44	-0.26	0.005	3.94	1.81	6.07	<0.001	0.77	0.33	1.21	0.001
6	-429805.60	-634713.90	-224897.40	<0.001	-0.89	-1.46	-0.31	0.003	4.14	2.03	6.24	<0.001	0.80	0.36	1.24	<0.001
7	-455630.00	-655226.80	-256033.10	<0.001	-0.92	-1.48	-0.36	0.001	4.30	2.20	6.39	<0.001	0.84	0.40	1.27	<0.001
8	-477741.50	-674224.50	-281258.40	<0.001	-0.95	-1.50	-0.40	0.001	4.41	2.32	6.50	<0.001	0.88	0.44	1.33	<0.001
9	-496282.10	-691238.20	-301325.90	<0.001	-0.98	-1.53	-0.43	0.001	4.49	2.40	6.58	<0.001	0.94	0.49	1.39	<0.001
10	-511379.70	-705935.60	-316823.90	<0.001	-1.01	-1.56	-0.46	<0.001	4.52	2.43	6.61	<0.001	1.01	0.55	1.46	<0.001
11	-523149.30	-718133.90	-328164.60	<0.001	-1.04	-1.59	-0.49	<0.001	4.51	2.41	6.61	<0.001	1.09	0.62	1.55	<0.001
12	-531692.90	-727797.30	-335588.50	<0.001	-1.07	-1.62	-0.51	<0.001	4.46	2.35	6.57	<0.001	1.17	0.70	1.65	<0.001
13	-537101.00	-735025.40	-339176.60	<0.001	-1.10	-1.66	-0.53	<0.001	4.37	2.24	6.49	<0.001	1.27	0.79	1.76	<0.001
14	-539452.30	-740038.00	-338866.60	<0.001	-1.12	-1.70	-0.55	<0.001	4.23	2.09	6.38	<0.001	1.38	0.89	1.88	<0.001
15	-538814.60	-743156.40	-334472.90	<0.001	-1.15	-1.74	-0.56	<0.001	4.06	1.89	6.22	<0.001	1.51	0.99	2.02	<0.001
16	-535245.00	-744780.50	-325709.50	<0.001	-1.18	-1.78	-0.57	<0.001	3.84	1.64	6.04	0.001	1.64	1.11	2.17	<0.001
17	-528790.10	-745360.30	-312219.80	<0.001	-1.21	-1.83	-0.58	<0.001	3.58	1.34	5.81	0.002	1.78	1.23	2.34	<0.001
18	-519486.50	-745361.60	-293611.40	<0.001	-1.23	-1.87	-0.59	<0.001	3.28	0.99	5.56	0.005	1.94	1.36	2.52	<0.001
19	-507360.90	-745229.90	-269491.80	<0.001	-1.26	-1.92	-0.60	<0.001	2.93	0.58	5.28	0.014	2.11	1.51	2.72	<0.001
20	-492430.20	-745358.90	-239501.50	<0.001	-1.28	-1.97	-0.60	<0.001	2.55	0.12	4.97	0.040	2.30	1.65	2.94	<0.001
21	-474701.90	-746069.30	-203334.50	0.001	-1.31	-2.01	-0.60	<0.001	2.12	-0.41	4.64	0.100	2.49	1.81	3.18	<0.001
Joint				<0.001				<0.001				<0.001				<0.001

Open in a new tab

llb = low limit bound, ulb = lower limit bound.

Average Hb levels (Fig 3A and 3B) were significantly lower in non-survivors than in survivors (p-joint <0.001). The temporal analysis suggested that, at the time of symptoms’ onset, the difference between survivors and non-survivors was marginal (p = 0.063) but became statistically significant by the next day (p = 0.041). The average levels of Hb were steadily within the normal range in survivors while a mild (non-statistically significant) anaemia (Hb level <10 mg/dL) was observed at the end of follow-up in non-survivors. Among the considered confounders, age (p<0.001) and chronic renal failure (p<0.001) were significantly associated to lower Hb level. No significant association was found for obesity and cardiovascular diseases.

Average MCVs (Fig 3C and 3D) were significantly higher in non-survivors than in survivors (p-joint <0.001). The temporal analysis suggested that, at the time of symptoms’ onset, MCV difference between survivors and non-survivors was marginal (p = 0.066) but became statistically significant by the next day (p = 0.024). Although average of MCVs was higher in non-survivors than in survivors, the variations of MCV were always within the normal range in both survivors and non-survivors. Among the considered confounders, chronic renal failure was associated with higher MCV (p = 0.026) and cardiovascular diseases were associated with lower MCV (p = 0.013), while no significant association was found for age and obesity.

Average RDW percentages (Fig 3E and 3F) were significantly higher in non-survivors than in survivors (p-joint <0.001). The temporal analysis suggested that this difference was already significant since the first day of symptoms’ onset (p = 0.004). The average percentages of RDW were steadily within the normal range in survivors while a mild (non-statistically significant) increase of anisocytosis (RDW >16%) was observed at the end of follow-up in non-survivors. Among the considered confounders, age, chronic renal failure and cardiovascular diseases were significantly associated to higher RDW percentage (p<0.001 for the three confounders) while no significant association was found for obesity.

Platelets parameters and kinetics

Two platelets parameters (i.e. platelets’ counts and MPV) were analysed (Fig 4 and Table 4). Average platelets’ counts (Fig 4A and 4B) were significantly different between non-survivors and survivors (p-joint <0.001). The temporal analysis suggested that platelets’ counts were lower in survivors than in non-survivors at beginning of the diseases, while the opposite was evident at the end of the follow-up. In survivors, platelets’ counts peaked at 302,758 cells/mm³ (95% CI: 264,219–341,298) on day 19 and eventually remained steady. In contrast, platelets’ counts in non-survivors peaked at 223,850 cells/mm³ (95% CI: 190,457–257,242) on day 9 and eventually decreased, being significantly below the lower normal limit value (150,000 cells/mm³) at the end of follow-up. Among confounders, only age was significantly associated to lower platelets’ count (p = 0.001) while no significant association was found for obesity, chronic renal failure and cardiovascular diseases.

Table 4. Temporal evolution of the differences of platelets’ counts and MPV, between survivors and non-survivors (diff).

	Platelets’ counts (cells/mm3)				MPV (fL)
Day	diff	llb	ulb	p	diff	llb	ulb	p
0	47337.03	7861.02	86813.05	0.019	-0.92	-1.43	-0.40	0.001
1	48510.89	9369.23	87652.56	0.015	-0.79	-1.26	-0.32	0.001
2	48464.84	9983.32	86946.37	0.014	-0.66	-1.10	-0.22	0.003
3	46982.17	9414.44	84549.90	0.014	-0.53	-0.93	-0.12	0.012
4	43871.02	7384.70	80357.34	0.018	-0.39	-0.78	-0.01	0.045
5	38976.60	3645.00	74308.21	0.031	-0.26	-0.62	0.11	0.167
6	32192.67	-2007.41	66392.76	0.065	-0.12	-0.47	0.23	0.500
7	23471.41	-9712.96	56655.77	0.166	0.02	-0.32	0.36	0.922
8	12831.03	-19536.53	45198.58	0.437	0.16	-0.18	0.49	0.360
9	360.46	-31457.88	32178.80	0.982	0.30	-0.03	0.63	0.079
10	-13779.49	-45366.39	17807.41	0.393	0.44	0.11	0.78	0.009
11	-29358.67	-61060.23	2342.90	0.070	0.59	0.25	0.93	0.001
12	-46083.82	-78250.46	-13917.18	0.005	0.74	0.40	1.09	<0.001
13	-63608.35	-96570.40	-30646.29	<0.001	0.89	0.54	1.25	<0.001
14	-81545.08	-115590.40	-47499.80	<0.001	1.05	0.69	1.42	<0.001
15	-99481.31	-134837.10	-64125.49	<0.001	1.21	0.83	1.59	<0.001
16	-116995.30	-153816.80	-80173.76	<0.001	1.37	0.97	1.78	<0.001
17	-133673.30	-172039.10	-95307.43	<0.001	1.54	1.11	1.97	<0.001
18	-149125.80	-189040.40	-109211.20	<0.001	1.72	1.25	2.18	<0.001
19	-163003.00	-204405.00	-121601.00	<0.001	1.89	1.39	2.39	<0.001
20	-175007.30	-217781.90	-132232.70	<0.001	2.07	1.53	2.62	<0.001
21	-184903.40	-228896.60	-140910.30	<0.001	2.26	1.66	2.86	<0.001
Joint				<0.001				<0.001

Open in a new tab

llb = low limit bound, ulb = lower limit boun.

Average MPV (Fig 4C and 4D) had patterns of temporal variations similar to those observed for platelets’ counts. MPV was significantly higher in survivors than in non-survivors at the beginning of the disease (p<0.001) while the opposite was observed at the end of the follow-up, with MPV significantly higher in non–survivors than in survivors (p<0.001). MPV tended to normalize over time in survivors, it steadily increased in non-survivors, exceeding the upper normal limit value (i.e. 11 fl) by the day 7 after symptoms’ onset. Among the considered confounders, only chronic renal failure is significantly associated to higher MPV (p = 0.022) while no significant association was found for age, obesity, and cardiovascular diseases.

Discussion

We found that increased neutrophil counts, reduced lymphocyte counts, increased MPV, anaemia with anisocytosis, in association with obesity, chronic renal failure, COPD, cardiovascular diseases and age >60 years are poor prognostic indicators for COVID19.

The first analysed CBC set was leukocytes. Whilst in non-survivors lymphocytes’ counts were persistently below the lower normal limit, in survivors lymphopenia was (on average) only mild and transient. This observation suggests that lymphopenia can be considered a surrogate biomarker of ineffective immune response to the SARS-CoV-2 [12] infection, as observed for other coronavirus including MERS [13] and SARS-CoV-1 [14]. The biological reasons associated to lymphopenia are under investigation and whilst this could be due to localisation at sites of disease migrating from peripheral blood, it may be also be due to dysregulation of the cytokine network [15]. Neutrophils’ counts showed an opposite trend, significantly increasing during the second week of the disease in non-survivors, while they remained within the normal range in survivors. The strong association between patients’ clinical outcome and increase of neutrophil counts was a biologically and clinically relevant finding of our study and the reasons for that are manifold. Although bacterial infections could not be ruled out in this study, the inverse relationship between neutrophils’ and lymphocytes’ counts with disease progression could have other explanations. First, the opposite kinetics in neutrophils versus lymphocytes dynamics might be driven by depletion of CD8+ circulating lymphocytes and a concomitant increased release of serum IL-6, IL-10, IL-2 and IFN-γ in patients with severe disease presentations as compared to patients with moderate disease [16]. Second, the high neutrophils may be due to a massive expansion of myeloid cells with suppressor activity [granulocytes myeloid suppressor cells-MDSC]. These cells are morphologically identical to neutrophils in standard laboratory examination but they are functionally distinct. MDSC are characterized by their myeloid origin, immature state, and by their potent ability to suppress T-cell functions and to modulate cytokines’ production [17]. According to this hypothesis, the hyper inflammatory state observed in severe COVID-19 cases, may induce an abnormal expansion of MDSC that in turn strongly affect the immunological response required for viral clearance [18,19].

The second CBC set included platelets’ counts and MPV. The variation of both parameters was consistent with acute inflammation followed with either by recovery or by death. The model predicted that mild thrombocytopenia was present in both survivors and non-survivors. However, while platelets’ level had a steady trend toward normalization in survivors, platelets’ counts irreversibly fell during the second week of the diseases in non-survivors, possibly because of a consumption related to the development of micro-thrombotic events in small vessels. The falling platelets’ counts in non-survivors was paralleled with a progressive increase of MPV suggesting that the increased platelets’ consumption and the production of pro-inflammatory cytokines led to an increased, yet insufficient, megakaryocytic activity with release in the circulation of young, large volume platelets [20]. Our results concur with a recent metanalysis of nine studies with 1779 COVID-19 patients, showing that low platelets’ counts were associated with increased risk of severe disease and mortality [21]. The precise mechanisms by which SARS-CoV-2 affects the hematopoietic system in general and platelets in particular are yet unclear. Reduced production, augmented destruction and augmented consumption have been suggested [22,23].

The third set of CBC parameters were RBC counts, HB, MCV and RDW. Our model suggested that variation of the four analysed RBC parameters were marginal and with few clinical implications. The level of RBC counts, HB and MCV were generally within normal range and difference found between survivors and non-survivors were most probably due to small systematic differences in the treatment rather than a direct consequence of the COVID-19 its-self. Notably model predicted higher RDW levels in non-survivors than in survivors. This observation was consistent with current knowledge that identified RDW as a marker of dysregulated inflammatory response [24], a negative prognostic index for patients with pneumonia [25], and a predictor of severe lung injury [26,27].

The emerged significant associations between potential confounders and CBC parameters might have several different explanations. The association between age and alteration of platelets’ and WBCs’ parameters (mainly lymphopenia), probably reflects the severity of COVID-19 [5,28]. Instead, the association between age, chronical renal failure and cardiovascular diseases with the alteration of the RBCs’ parameters, mainly anaemia and anisocytosis, probably reflects the stage of the chronical conditions [29,30].

Whilst we modelled data by techniques considered robust, previously validated for analysing complex datasets made of sparse repeated measures in different fields of health [31] and clinical infectious diseases research [11,32–34], there are limitations of our study, inherent to observational studies carried out on data collected in real-time inpatients’ clinical management. The study population was represented in majority by males (273, 72%) and this might have impacted on the interpretation of the results. In fact, clinical outcomes can be influenced by pre-existing comorbidities, such as hypertension, cardiovascular disease and diabetes, which tended to be more frequent and more severe in men [35]. Another limitation was analysing only CBC parameters without other laboratory/clinical signs of infection, and without considering different treatment provided. Moreover, although we found significant associations between clinical outcome and haematological parameters, a causal relationship cannot be inferred.

Despite the above limitations, our study describes for the first time the association between haematological parameters and COVID-19 clinical outcomes, through the analysis of routinely collected clinical and laboratory data. Although we exercise caution in the interpretation of results using ‘routine’ laboratory parameters, our findings point out very relevant factors in the dynamic nature of COVID-19 pathophysiology. Poor prognosis is associated with increased neutrophils counts, reduced lymphocytes counts, increased MPV and anaemia with anisocytosis. Even in the absence of deeper analysis of cytokine levels and immune cell subsets, the inverse relationship between neutrophils and lymphocytes shed light on pivotal factors in immune dysregulation and impairment to fight off SARS-CoV-2.These observations have direct implication for future development of more accurate prognostic indexes, composite outcomes for interventional studies on new drugs and for endorsing new functional studies to assess the role of leukocytes and platelets in the pathogenies of severe infections, such as COVID-19.

Supporting information

S1 File. Full STATA statistical analysis.

(PDF)

Click here for additional data file.^{(632.4KB, pdf)}

S2 File. Model building and variable selection process.

(PDF)

Click here for additional data file.^{(290.8KB, pdf)}

S1 Dataset

(XLSX)

Click here for additional data file.^{(1.8MB, xlsx)}

Acknowledgments

The study has been carried out with the support of the INMI COVID-19 Study Group.

Members: Maria Alessandra Abbonizio, Chiara Agrati, Fabrizio Albarello, Gioia Amadei, Alessandra Amendola, Mario Antonini, Raffaella Barbaro, Barbara Bartolini, Martina Benigni, Nazario Bevilacqua, Licia Bordi, Veronica Bordoni, Marta Branca, Paolo Campioni, Maria Rosaria Capobianchi, Cinzia Caporale, Ilaria Caravella, Fabrizio Carletti, Concetta Castilletti, Roberta Chiappini, Carmine Ciaralli, Francesca Colavita, Angela Corpolongo, Massimo Cristofaro, Salvatore Curiale, Alessandra D'Abramo, Cristina Dantimi, Alessia De Angelis, Giada De Angelis, Rachele Di Lorenzo, Federica Di Stefano, Federica Ferraro, Lorena Fiorentini, Andrea Frustaci, Paola Gallì, Gabriele Garotto, Maria Letizia Giancola, Filippo Giansante, Emanuela Giombini, Maria Cristina Greci, Giuseppe Ippolito, Eleonora Lalle, Simone Lanini, Daniele Lapa, Luciana Lepore, Andrea Lucia, Franco Lufrani, Manuela Macchione, Alessandra Marani, Luisa Marchioni, Andrea Mariano, Maria Cristina Marini, Micaela Maritti, Markus Maeurer, Giulia Matusali, Silvia Meschi, Francesco Messina, Chiara Montaldo, Silvia Murachelli, Emanuele Nicastri, Roberto Noto, Claudia Palazzolo, Emanuele Pallini, Virgilio Passeri, Federico Pelliccioni, Antonella Petrecchia, Ada Petrone, Nicola Petrosillo, Elisa Pianura, Maria Pisciotta, Silvia Pittalis, Costanza Proietti, Vincenzo Puro, Gabriele Rinonapoli, Martina Rueca, Alessandra Sacchi, Francesco Sanasi, Carmen Santagata, Silvana Scarcia, Vincenzo Schininà, Paola Scognamiglio, Laura Scorzolini, Giulia Stazi, Francesco Vaia, Francesco Vairo, Maria Beatrice Valli, Alimuddin Zumla.

GI and AZ are co-principal investigators of the Pan-African Network on Emerging and Re-Emerging Infections, which is funded by the EDCTP, the European Union Horizon 2020 Framework Programme for Research and Innovation; and is also in receipt of a NIH Research senior investigator award.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by Line one-Ricerca Corrente ‘Infezioni Emergenti e Riemergenti’ and by Progetto COVID 2020 12371675 both funded by Italian Ministry of Health; AIRC (IG2018-21880); Regione Lazio (Gruppi di ricerca, E56C18000460002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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30.Clerkin KJ, Fried JA, Raikhelkar J, Sayer G, Griffin JM, Masoumi A, et al. COVID-19 and Cardiovascular Disease. Circulation. 2020. May 19;141(20):1648–1655. 10.1161/CIRCULATIONAHA.120.046941 [DOI] [PubMed] [Google Scholar]
31.Trynke Hoekstra, Jos W. R. Twisk. A Life Course Perspective on Health Trajectories and Transitions Chapter 9: The Analysis of Individual Health Trajectories Across the Life Course: Latent Class Growth Models Versus Mixed Models. Available from: https://www.ncbi.nlm.nih.gov/books/NBK385363/. [PubMed]
32.Pironti A, Walter H, Pfeifer N, Knops E, Lübke N, Büch J, et al. Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data. J Acquir Immune Defic Syndr. 2017. April 15;74(5):e129–e137. 10.1097/QAI.0000000000001198 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Lanini S, Portella G, Vairo F, Kobinger GP, Pesenti A, Langer M, et al. Relationship Between Viremia and Specific Organ Damage in Ebola Patients: A Cohort Study. Clin Infect Dis. 2018. January 6;66(1):36–44. 10.1093/cid/cix704 [DOI] [PubMed] [Google Scholar]
34.Reisler RB, Kraft CS, Bavari S, Cardile AP. Clinical Laboratory Values in Human Ebola Virus Disease Support the Relevance of the Intramuscular Ebola-Kikwit Rhesus Model. Clin Infect Dis. 2018;66(9):1479–1480. 10.1093/cid/cix1025 [DOI] [PubMed] [Google Scholar]
35.Ambrosino I, Barbagelata E, Ortona E, Ruggieri A, Massiah G, Giannico OV, et al. Gender differences in patients with COVID-19: a narrative review. Monaldi Arch Chest Dis. 2020. May 25;90(2). 10.4081/monaldi.2020.1389 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0244129.r001

Decision Letter 0

Silvia Ricci

Transfer Alert

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

26 Aug 2020

PONE-D-20-21231

COVID-19 Disease - Temporal analyses of complete blood count parameters over course of illness, and relationship to patient demographics and management outcomes in survivors and non-survivors: a longitudinal descriptive cohort study

PLOS ONE

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**********

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**********

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**********

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Reviewer #1: Lanini et al present the results of a temporal analyses of complete blood count parameters over course of COVID-19 illness, and relationship to patient demographics and management outcomes in survivors and non-survivors in a large Italian referral center. Please find below my comments:

1) The manuscript should be reviewed by the authors for minor grammar and syntax errors

2) Abstract and Results: please provide also absolute values instead of reporting only p-values

3) Abstract: Please specify the "reverse temporal trends".

4) Abstract: Please define the "average" estimates in the methods section

5) Abstract and Discussion: "Increased neutrophilcounts, reduced lymphocytecounts, median platelet volume, anaemia with anisocytosis, in association with obesity, chronic renal failure,..." Please restate the phrase "in association with"

6) Please provide figures of better quality

7) Please define "history of neoplasm". Does it mean current chemotherapy or those receiving chemotherapy and those under surveillance are included? Do you have any data regarding the patients currently receiving chemotherapy (eg last 14 days from symptom onset) compared to the others?

8) Please expand the Table legends and state that the reported differences are between survivors and non-survivors

9) Please add units in the Tables, as appropriate

10) Please consider supporting the rationale of the study in the introduction and discussion (eg consider this review E Terpos et al. Hematological findings and complications of COVID-19. Am J Hematol . 2020 Jul;95(7):834-847. doi: 10.1002/ajh.25829. Epub 2020 May 23. )

11) Do you have any data regarding CRP and PCT values in different time points in order to correlate them with the corresponding neutrophil counts?

12) In the limitations please make a comment regarding the 72% male percentage in the study cohort and if this has any impact on the interprentation of the results

13) Several significant associations have emerged between potential confounders and CBC parameters, as described in the results. Please comment and elaborate more on potential explanations in the discussion.

Reviewer #2: General comments:

- the manuscript can be shortened substantially, especially the results and the discussion. In fact, the results are presented in a very descriptive way, and are already partly discussed.

- as stated in the background section, “complete blood count provides vital parameters which can inform presence of infection, response to treatment, …”. In the following analysis, laboratory/clinical signs of infection and different treatment were not considered. I suggest highlighting it among the limits of the study.

- editing for grammar and language will be helpful.

Specific comments:

- study design: I am still a bit confused by the protocol - were laboratory tests performed with fixed timing (e.g. once per week)? If not, how many blood tests per patient were considered at least and at most? In fact, there is a very interesting day by day analysis (resumed on the Tables), but there is no mention of the number of tests considered for each single day. I am concerned that this might impact the strength of the results for the first days after symptom onset, when probably just a minority of patients was attended.

- discussion section: “data were collected daily in inpatients with COVID-19 disease until time to discharge or death” – this information is in contrast with what stated in the methods and results section (mean of 4.4 laboratory tests per patient – tested for 21 days after symptom onset).

- conclusions might be presented in a more appropriate fashion: e.g. the first part of the last paragraph (“our study describes for the first time the evolution of COVID-19 illness over time”) is not supported by the data presented. A clear conclusion section might be helpful, with a synthetic resume of the most important findings.

- legends to figures are too long and repetitive.

- tables: there are no explanations for the acronyms used, i.e. llb and ulb. Moreover, I couldn’t find a reference for Table 2 inside the text.

**********

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Reviewer #2: No

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PLoS One. 2020 Dec 28;15(12):e0244129. doi: 10.1371/journal.pone.0244129.r002

Author response to Decision Letter 0

30 Oct 2020

Reviewer #1:

Lanini et al present the results of a temporal analyses of complete blood count parameters over course of COVID-19 illness, and relationship to patient demographics and management outcomes in survivors and non-survivors in a large Italian referral center. Please find below my comments:

1) The manuscript should be reviewed by the authors for minor grammar and syntax errors.

We have reviewed and corrected grammar and syntax errors (corrections marked in track changes in the manuscript).

2) Abstract and Results: please provide also absolute values instead of reporting only p-values.

According to the request of the reviewer, we have added OR and 95%CI for the bivariable and multivariable analysis results (as for table 1) (current lines: 56 – 62). However, we have left just the p-value of the differences between survivors and non-survivors for temporal analysis (mixed effect models). In fact, these p-values assess whether there is a significant difference in the temporal trend for each individual parameter between survivors and non-survivors as a whole.

3) Abstract: Please specify the "reverse temporal trends".

We agree with the reviewer that the statement about “reverse temporal trends” in the abstract is redundant and might be confusing. In fact, the observed different trend has been already reported in the previous sentence. We removed the statement (current line 52).

4) Abstract: Please define the "average" estimates in the methods section.

In the methods section of the abstract, as per reviewer recommendation, we have defined the model-based punctual estimates, as average of all patients’ values (current lines 43 - 44).

5) Abstract and Discussion: "Increased neutrophil counts, reduced lymphocyte counts, median platelet volume, anaemia with anisocytosis, in association with obesity, chronic renal failure,..." Please restate the phrase "in association with".

As per reviewer’s suggestion, we have restated the phrase reformulating it as follows: “Increased neutrophil counts, reduced lymphocyte counts, increased median platelet volume and anaemia with anisocytosis, are poor prognostic indicators for COVID19, after adjusting for the confounding effect of obesity, chronic renal failure, COPD, cardiovascular diseases and age >60 years” (current lines 65- 68).

6) Please provide figures of better quality:

We have provided all the figures (1, 2, 3, 4) in TIFF format.

As per reviewer recommendation, we have added in the manuscript the definition of “history of neoplasm” defined as “any anamnestic data of oncological disease” (current line 183).

Nineteen patients of the cohort had a positive anamnesis for oncological diseases. None of them received chemotherapy during the admission for COVID-19 and/or in the 14 days before the symptoms’ onset. Out of these 19 patients, 5 died.

8) Please expand the Table legends and state that the reported differences are between survivors and non-survivors.

According to the reviewer’s indications, we have expanded the tables’ legends stating that the reported differences are between survivors and non-survivors, in tables 2, 3 and 4.

9) Please add units in the Tables, as appropriate.

According to the reviewer’s indications, we have added the units in the tables 1, 2, 3 and 4.

We have added in the background a sentence supporting the rational of the study, based on the review indicated by the reviewer stating that: “As underlined by a recent review, COVID‐19 has a significant impact on the hematopoietic system: lymphopenia, neutrophil/lymphocyte ratio and peak platelet/lymphocyte ratio may be considered as cardinal laboratory findings, with prognostic potential” (current lines 77 – 80), and we have added the related reference (ref. 5).

11) Do you have any data regarding CRP and PCT values in different time points in order to correlate them with the corresponding neutrophil counts?

Data regarding CRP and PCT values in different time points would have definitively been extremely interesting. Unfortunately, we didn’t have the possibility to analyze these data, and we have mentioned it as a limitation of the study (current lines 337—339).

12) In the limitations please make a comment regarding the 72% male percentage in the study cohort and if this has any impact on the interpretation of the results.

As suggested by the reviewer, in the discussion we have added the higher percentage of males as possible limitation of the study, explaining that “clinical outcomes can be influenced by pre-existing comorbidities, such as hypertension, cardiovascular disease and diabetes, which tended to be more frequent and more severe in men” (current lines 335 – 337) and we have added a related reference (ref 34): Ambrosino I, Barbagelata E, Ortona E, et al. Gender differences in patients with COVID-19: a narrative review. Monaldi Arch Chest Dis. 2020;90(2):10.4081/monaldi.2020.1389. Published 2020 May 25.

We thank the reviewer for this remark which allowed us to add potential explanations regarding the association between potential confounders and CBC parameters at the end of the discussion (current lines 324 – 329): “The emerged significant associations between potential confounders and CBC parameters might have several different explanations. The association between age and alteration of WBCs’ and platelets’ parameters, mainly lymphopenia, probably reflects the severity of COVID-19. Instead, the association between age, chronical renal failure and cardiovascular diseases with the alteration of the red blood cells’ parameters, mainly anemia and anisocytosis, probably reflects the stage of the chronical conditions”.

In support of these explanations we have added 3 new references:

27. Linton, P., Dorshkind, K. Age-related changes in lymphocyte development and function. Nat Immunol 5, 133–139 (2004). https://doi.org/10.1038/ni1033

28. M. Ajaimy and M.L. Melamed. COVID-19 in Patients with Kidney Disease. CJASN 15: 1087–1089, Vol 15 August, 2020

29. Clerkin KJ, Fried JA, Raikhelkar J, et al. COVID-19 and Cardiovascular Disease. Circulation. 2020;141(20):1648-1655. doi:10.1161/CIRCULATIONAHA.120.046941

Reviewer #2:

General comments

1) The manuscript can be shortened substantially, especially the results and the discussion. In fact, the results are presented in a very descriptive way, and are already partly discussed.

According to the reviewer’s suggestion, we have shortened results and discussion, eliminating the overlapping parts.

2) As stated in the background section, “complete blood count provides vital parameters which can inform presence of infection, response to treatment, …”. In the following analysis, laboratory/clinical signs of infection and different treatment were not considered. I suggest highlighting it among the limits of the study.

As suggested by the reviewer, we have highlighted that analyzing only CBC parameters without other laboratory/clinical signs of infection, and without considering different treatment provided, represents a limitation of the study (lines 337 – 339).

3) Editing for grammar and language will be helpful.

We have reviewed and corrected grammar and syntax errors (corrections marked in track changes in the manuscript).

Specific comments:

4) Study design: I am still a bit confused by the protocol - were laboratory tests performed with fixed timing (e.g. once per week)? If not, how many blood tests per patient were considered at least and at most? In fact, there is a very interesting day by day analysis (resumed on the Tables), but there is no mention of the number of tests considered for each single day. I am concerned that this might impact the strength of the results for the first days after symptom onset, when probably just a minority of patients was attended.

Thank you for this question that permitted us to better emphasize technical issues of the statistical model. As correctly suggested by the reviewer, the number of measures vary among patients. To deal with this issue we have used a mixed effect multilevel model (MEML) for unbalanced samples. This kind of models are among the most robust statistical techniques for dealing with sparse repeated measures that usually come from clinical dataset made of routine collected data.

In particular our model has a random coefficient for intercept for dealing with correlation at the level of the patients; a random slope coefficient at the level of time for dealing with correlation between time and the random intercept; an unstructured covariance matrix coefficient that is specifically set for dealing with potential additional issues due to unbalanced sampling, consequent to unequal number of measures for the different subjects (i.e no assumption on variance-covariance structure was made).

We have already validated this model in previous studies that dealt with repeated spare measures:

• Lanini S, Bartolini B, Taibi C, et al. Early improvement of glycaemic control after virus clearance in patients with chronic hepatitis C and severe liver fibrosis: a cohort study. New Microbiol. 2019;42(3):139-144.

• Lanini S, Portella G, Vairo F, et al. Relationship Between Viremia and Specific Organ Damage in Ebola Patients: A Cohort Study. Clin Infect Dis. 2018;66(1):36-44. doi:10.1093/cid/cix704

• Lanini S, Portella G, Vairo F, et al. Blood kinetics of Ebola virus in survivors and nonsurvivors. J Clin Invest. 2015;125(12):4692-4698. doi:10.1172/JCI83111

According to the reviewer’s suggestion, statistical methods has been expanded to report number of observations per patients and per time point (current lines 141 – 143).

5) Discussion section: “data were collected daily in inpatients with COVID-19 disease until time to discharge or death” – this information is in contrast with what stated in the methods and results section (mean of 4.4 laboratory tests per patient – tested for 21 days after symptom onset).

We thank the reviewer to have highlighted an incorrect sentence that gave an inconsistent message. In fact we have analyzed the tests performed within the first 21 days after the symptoms’ onset (not until time of discharge or death). According to the reviewer’s comment, we have removed the first sentence since it is incorrect and since the correct information is already provided in the method section (current lines 140 – 143).

6) Conclusions might be presented in a more appropriate fashion: e.g. the first part of the last paragraph (“our study describes for the first time the evolution of COVID-19 illness over time”) is not supported by the data presented. A clear conclusion section might be helpful, with a synthetic resume of the most important findings.

We have edited the conclusion according with the reviewer suggestions as follows (lines 342 – 353): “Despite the above limitations, our study describes for the first time the association between haematological parameters and COVID-19 clinical outcomes, through the analysis of routinely collected clinical and laboratory data. Although we exercise caution in the interpretation of results using ‘routine’ laboratory parameters, our findings point out very relevant factors in the dynamic nature of COVID-19 pathophysiology. Poor prognosis is associated with increased neutrophils counts, reduced lymphocytes counts, increased MPV and anaemia with anisocytosis. Even in the absence of deeper analysis of cytokine levels and immune cell subsets, the inverse relationship between neutrophils and lymphocytes shed light on pivotal factors in immune dysregulation and impairment to fight off SARS-CoV-2. These observations have direct implication for future development of more accurate prognostic indexes, composite outcomes for interventional studies on new drugs and for endorsing new functional studies to assess the role of leukocytes and platelets in the pathogenies of severe infections, such as COVID-19”.

7) Legends to figures are too long and repetitive.

As suggested by the reviewer, we have shorten the legends of the figures deleting repetitive parts already mentioned in the main article.

8) Tables: there are no explanations for the acronyms used, i.e. llb and ulb. Moreover, I couldn’t find a reference for Table 2 inside the text.

As suggested by the reviewer, we have added a legend to explain the acronyms in tables 2, 3 and 4: llb= low limit bound, ulb= lower limit bound.

The reference to table 2 is in the results section in the chapter: Leukocytes parameters kinetics (current line 192).

Attachment

Submitted filename: Response to Reviewers.pdf

Click here for additional data file.^{(360.4KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0244129.r003

Decision Letter 1

Silvia Ricci

4 Dec 2020

PONE-D-20-21231R1

Dear Dr. Simone Lanini

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

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

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Kind regards,

Silvia Ricci

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

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

Reviewer #1: (No Response)

Reviewer #2: I Don't Know

**********

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

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

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

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: Thank you for addressing all the comments.

Still, I do not feel familiar with the acronyms you used in the Tables: llb= low limit bound, ulb= lower limit bound. Standing on the numbers reported, it looks like if "ulb" represented the upper limit bound. In any case, I would suggest to further explain the significance of llb and ulb in that context.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0244129.r004

Acceptance letter

Silvia Ricci

16 Dec 2020

PONE-D-20-21231R1

Dear Dr. Lanini:

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

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

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

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Silvia Ricci

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Full STATA statistical analysis.

(PDF)

Click here for additional data file.^{(632.4KB, pdf)}

S2 File. Model building and variable selection process.

(PDF)

Click here for additional data file.^{(290.8KB, pdf)}

S1 Dataset

(XLSX)

Click here for additional data file.^{(1.8MB, xlsx)}

Attachment

Submitted filename: Response to Reviewers.pdf

Click here for additional data file.^{(360.4KB, pdf)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

[pone.0244129.ref001] 1.Novel Coronavirus–China Disease outbreak news: Update 12 January 2020. Available from: https://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/.

[pone.0244129.ref002] 2.WHO Naming the coronavirus disease (COVID-19) and the virus that causes it.

[pone.0244129.ref003] 3.WHO 2020: Coronavirus disease (COVID-2019) situation reports. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/.

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PERMALINK

COVID-19 disease—Temporal analyses of complete blood count parameters over course of illness, and relationship to patient demographics and management outcomes in survivors and non-survivors: A longitudinal descriptive cohort study

Simone Lanini

Chiara Montaldo

Emanuele Nicastri

Francesco Vairo

Chiara Agrati

Nicola Petrosillo

Paola Scognamiglio

Andrea Antinori

Vincenzo Puro

Antonino Di Caro

Gabriella De Carli

Assunta Navarra

Alessandro Agresta

Claudia Cimaglia

Fabrizio Palmieri

Gianpiero D’Offizi

Luisa Marchioni

Gary Pignac Kobinger

Markus Maeurer

Enrico Girardi

Maria Rosaria Capobianchi

Alimuddin Zumla

Franco Locatelli

Giuseppe Ippolito

Roles

Abstract

Background

Methods

Main findings

Interpretation

Background

Methods

Study design

Setting

Ethics/IRB approval

Patients and follow-up

Variables studied

Definitions

Data collection and data quality assessment

Laboratory methods

Statistics and modelling

Results

Patients recruited and demographics

Fig 1. Selection of patients.

Table 1. Patients’ demographics and co-morbidities.

Association between patients baseline conditions and clinical outcome

Leukocytes parameters kinetics

Fig 2.

Table 2. Temporal evolution of the differences of neutrophils’, lymphocyte’s and monocytes’ counts, between survivors and non-survivors (diff).

Red blood cells parameters and kinetics

Fig 3.

Table 3. Temporal evolution of the difference of red cell count, Haemoglobin level, MCV and RDW between survivors and non-survivors (diff).

Platelets parameters and kinetics

Fig 4.

Table 4. Temporal evolution of the differences of platelets’ counts and MPV, between survivors and non-survivors (diff).

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Silvia Ricci

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Silvia Ricci

Roles

Acceptance letter

Silvia Ricci

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES