The effect of malnutrition on adult Covid-19 patient’s ICU admission and mortality in Covid-19 isolation and treatment centers in Ethiopia: A prospective cohort study

Lencho Mekonnen Jima; Gudina Egeta Atomsa; Johane P Allard; Yakob Desalegn Nigatu

doi:10.1371/journal.pone.0298215

. 2024 Mar 20;19(3):e0298215. doi: 10.1371/journal.pone.0298215

The effect of malnutrition on adult Covid-19 patient’s ICU admission and mortality in Covid-19 isolation and treatment centers in Ethiopia: A prospective cohort study

Lencho Mekonnen Jima ¹, Gudina Egeta Atomsa ¹, Johane P Allard ², Yakob Desalegn Nigatu ^1,^*

Editor: Nitai Roy³

PMCID: PMC10954135 PMID: 38507372

Abstract

Background

A new coronavirus was first identified in Wuhan, China in December 2019. Since the times of the 1918 influenza pandemic, malnutrition has been known as a risk factor for severity and mortality from viral pneumonia. Similarly, the recently identified SARS-Cov2 infection (COVID-19) and related pneumonia may be closely linked to malnutrition. Therefore, this study will contribute to new knowledge and awareness of the recording and evaluation of each COVID-19 patient’s nutritional status by assessing the effect of malnutrition on ICU admission and death of COVID-19 patients in developing countries.

Method

We conducted a prospective cohort study in adult COVID-19 patients admitted to selected COVID-19 Isolation and Treatment Centers, Addis Ababa, Ethiopia. Baseline data of the patients were collected using interviewer-administered structured questionnaire and data on the adverse outcomes of follow up were extracted from follow up chart. The main clinical outcomes (ICU admission and death) were captured every week of follow up. We ran a multivariate Cox’s regression analysis to determine the relationship between malnutrition at admission and its effect on ICU admission and death.

Results

A total of 581 COVID-19 patients were enrolled. From the total of recruited patients, 346 (59.6%) were males and 235 (40.4%) were females. The mean age of the respondents was 55 years (16.45) years. The Cox proportional hazard model controlled for sex, age group, number of co-morbidities, and number of medications found that malnutrition at admission was associated with ICU admission and death. When compared to well-nourished patients, the rate of ICU admission was significantly associated and found to be higher among underweight [(adjusted hazard ratio (AHR) = 10.02, 95% CI: (8.64–12.10)] and overweight [(AHR = 7.7, 95% CI: (6.41–9.62)] patients. The rate of survival probability was significantly associated and was found to be better among well-nourished patients (AHR = 0.06, 95% CI : (0.01–0.44) when compared with malnourished COVID-19 patients.

Conclusion

Malnutrition at the time of admission was shown to increase the risk of ICU admission and mortality among COVID-19 patients. Therefore, it is vital to evaluate patients’ nutritional condition early in their admission and provide timely intervention to minimize the effects on patients and the healthcare system.

Introduction

A new coronavirus was first identified in China in December 2019, causing an outbreak of pneumonia (COVID-19). Although most patients present with mild symptoms and quickly recover, the disease can cause severe respiratory disease, multiple organ dysfunction, and even death [1]. Since the times of the 1918 influenza pandemic [2], malnutrition has been known as a risk factor for severity and mortality from viral pneumonia. Similarly, the recently identified SARS-Cov2 infection and related pneumonia (COVID-19) may be closely linked to malnutrition [3, 4]. Indeed, a variety of characteristics found in COVID-19 patients are likely to result in body weight loss and malnutrition [5]. COVID-19 is a condition marked by an inflammatory syndrome, contributing to decreased food consumption and enhanced muscle catabolism, which is why patients with COVID-19 are at an elevated risk of malnutrition, making avoidance of malnutrition and dietary control important aspects of treatment [6].

In Ethiopia, having different nutritional problems such as underweight, micronutrient deficiency, and being overweight can all have an impact on COVID-19 patient adverse outcomes. Poor nutritional status with an increment of the prevalence and severity of malnutrition among COVID-19 patients can lead to a higher rate of intensive care unit (ICU) admission and longer stays in intensive care [7, 8]. Similarly, an overweight and obese COVID-19 patient raises the risk of hospitalization, ICU admission, intubation, and death [9–13].

As the result of literature and prior research indicates, being a malnourished COVID-19 patient can lead to a higher rate of ICU admission and increased death. Given the limited evidence in Africa, this research assesses the effect of malnutrition on ICU Admission and death of COVID-19 patients to fill the gap in knowledge on the documentation and assessment of the nutritional status of each COVID-19 patient and the lack of adequate attention to the nutritional intervention of COVID-19 patients.

Materials and methods

Study setting

The study was conducted in the COVID-19 Isolation and Treatment Center of St Paul’s Hospital Millennium Medical College (SPHMMC), Eka Kotobe General Hospital (EKGH) and Millennium COVID-19 care center (MCCC). St. Paul Millennium Medical College Hospital is located in the capital city of Ethiopia, Addis Ababa. It is the second largest hospital in Ethiopia. Eka Kotobe General Hospital was the first hospital assigned to treat positive cases of COVID-19 in Ethiopia. It has the potential to admit 600 cases. It is located in Addis Ababa. Whereas Millennium COVID-19 care centers are temporary establishments prepared to handle an influx of COVID-19 cases.

Study design

We conducted a facility-based prospective cohort study from December 2021 to February 2022 in selected COVID-19 Isolation and Treatment Centers.

Eligibility criteria

All individuals included in this study tested positive for COVID-19 through confirmed rRT-PCR (real-time reverse transcriptase Polymerase Chain Reaction) testing and were aged 18 years or older. Participation required the provision of consent without further symptomatic evaluation for COVID-19. Exclusions from the study comprised critically ill patients with bilateral upper extremity amputations, as this condition posed challenges in assessing nutritional status upon admission

Sample size calculation

Sample size was calculated to determine the magnitude of effect between malnutrition at admission and adverse outcomes of COVID-19.We used a double population proportion formula and the following assumptions to calculate sample size to determine the relationship between malnutrition at admission and adverse outcomes; P1 (Proportion of ICU admission among malnourished patients) = 9.5%, P2 (Proportion of ICU admission among well-nourished patients) = 9.5% [14]. Also P1 and P2 assumed for death P1 (Proportion of death among malnourished patients) = 1.5%, P2 (Proportion of death among well-nourished patients) = 1.5% [15], Z α/2 = 1.96 at a 95% level of confidence, 5% margin of error. And r = 1 (the proportion of ICU admission and death among malnourished and well-nourished patients was taken as equal, or 1:1) and 10% non-response rate. Based on this assumption, a total of 581 COVID-19 patients were required in the study based on the highest sample size obtained from ICU admission prevalence.

Sampling procedures

The study was conducted at EKGH, MCCC, and SPHMMC, COVID-19 Isolation and Treatment Centers using a purposive sampling method. These selected centers are mainly working on COVID-19 patients with well-equipped ICU setup and larger admission capacity. Sample was collected proportionally from each center based on their admission capacity until the minimum sample size of the study fulfilled. Consecutive sampling method was used to enroll study participants by keeping inclusion and exclusion criteria consecutively until expected number of sample size was reached.

Data collection

The base line data were collected using interviewer administered questionnaire by a nurse who is working in triage and also objective weight and height measurement was taken place using electronic portable weight scale (seca scale) and stadio-meter for height. The principal investigator provides training, technical assistance, and measurement equipment to data collectors in each center. MUAC was measured on the non-dominant arm using non-stretchable plastic tape at the midpoint between the olecranon and the acromial process after the arm is flexed to 90 degrees at the elbow. To improve intra-observer reliability, data collectors were trained in relevant measuring techniques by the principal investigator until an acceptable level of technical error of measurement (TEM) was obtained. The technical error of measurement and coefficient of variation of weight, height, and girth of limbs (MUAC) for both inter-examiner and intra-examiner measurements were all within acceptable limits. This means, as indicated in the literature, acceptable TEM values should be of the order of 0.1 kg for weight, 3 mm for height, and 2 mm for girth of limbs [16]. Before starting the measurement, the measurement tools were calibrated. Also, body weight was measured to the nearest 0.1 kg with light clothing and no shoes, using an electronic portable scale (seca scale). To ensure measurement accuracy, the scale was checked for a zero reading before each weighing. Both MUAC and height were measured to the nearest 0.1 cm with a flexible non-stretch tape and a studio meter, respectively. Those used to categorize nutritional status as follows. Undernourished: COVID-19 case with BMI <18.5 kg/m²and for those critically ill patients who are not capable of measuring BMI the MUAC measurement of<23.7cm were considered as underweight [17].Overnourished:COVID-19 case with BMI >25 kg/m²and for those critically ill patients who are not capable of measuring BMI the MUAC measurement of ≥28.1cm were considered as overweight [17]. MUAC is also recommended for patients with ascites or edema in legs or trunk to gauge dry weight and BMI [18].Other studies confirm that MUAC correlated well with BMI and could be used to identify patients as underweight and overweight [19–22].

In addition, a periodic supervision was held by nutritionist who is working in hospital to gate the adverse outcomes of the patient every week during follow up. ICU admission and death were censored at 2 months, hence COVID-19 patients who were discharged earlier than follow-up time without adverse outcome or stayed in the hospital longer than 2 months were treated as censored observations.

Statistical analysis

Before entering the data, the questionnaire was reviewed for completeness. Data entry was carried out on a kobo toolbox. Then exported into Excel 2007 for cleaning and coding [see S1 File]. Finally, structured data was analyzed using the Social Sciences Statistical Package (SSPS) version 23. The patient cohort characteristics for continuous data were measured in terms of central tendency (mean or median) and dispersion (standard deviation or range). A frequency distribution was used for categorical data. A chi-square test was used to show variables having a statistically significant association with the nutritional status of the patients. A chi-square test of association was done between nutritional status and other variables. As a result, those having a p-value less than 0.05 were included in the analysis of Cox regression.

Descriptive analysis of survival data was presented graphically using the Kaplan-Meier (KM) estimator.

In accordance with the proportional hazard’s assumption, our analysis reveals that the hazard ratios for the examined variables remain constant over time. This finding is supported by the examination of Schoenfeld residuals and log-log plots, which indicate parallelism between survival curves. A Log-Rank test was used to compare the survival experiences of different categories of covariates. Multivariable Cox-regression analysis was used to control for possible confounding factors and identify risk factors for COVID-19 adverse outcomes. The adjusted hazard ratio (AHR) along with its 95% confidence interval was estimated to measure the strength of association. The level of statistical significance was set at a p-value less than 0.05. While checking multicollinearity, the result of variance inflation factor (VIF) value was near to 1, which indicates that there was no correlation between each independent variable.

Ethical consideration

The study received ethical approval from the Research Ethical Committee of the School of Public Health, Addis Ababa University. Also, from each COVID-19 care center’s institutional review board (IRB), an ethical clearance was obtained before data collection took place. During the data collection, written informed consent was obtained from each patient after explaining the objectives of the study. To maintain anonymity, identifiers like names were not included in the questionnaire. All measures to maintain human rights, including informed consent, the right to participate in the study, the right to privacy and confidentiality, and the right to prevention of any type of harm, were taken into consideration.

Result

Socio-demographics characteristics

A total of 581 patients were studied. Of these, nutritional status was assessed for 22% of the patients using MUAC, and for the remaining 78% using BMI. There was no case in children and adolescence while 174(29.9%) of cases were 65 years of age or older. The mean (±SD) age was 55 (±16.45) years old with 346(59.6%) being male and 235(40.4%) female, 527 (90.7%) were urban residents and 495 (85.2%) were married. The mean (±SD) monthly incomes of the respondents were 4531(±3642) birr and range was between 0–20,000 birr [see S1 Table].

Admission-related clinical characteristics of the patients

Table 1 shows admission-related clinical characteristics of the patients by malnutrition status. Among the participants, 289 individuals exhibited malnutrition, encompassing both over nutrition (204 cases) and under nutrition (85 cases), while 292 participants were classified as well-nourished. There were statistically significant association seen between nutritional status younger age, female sex, income, co morbidity and higher number of medication and. In the study population, 45% of participants under 65 were well-nourished, while 55% were undernourished. Conversely, among those aged 65 and above, 63% were well-nourished, and 37% were undernourished.. Malnutrition of female patients was higher than male in the cohort (60% vs 42%, p<0.001). Malnourished patients had a higher number of co morbidities compared to well-nourished patients (60.2% vs. 51.7%, p = 0.04). According to co morbidity category, 39.8% of malnourished patients had no co morbidity, whereas 60.2% had at least one co morbidity.

Table 1. General admission-related characteristics of the patients according to nutrition status at baseline among COVID-19 patients in SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Variables	Well nourished	Malnourished			P-value
(N = 581)	Frequency	Percent	Frequency	Percent
Age Group					<0.001*
<65	182	62.3%	225	77.9%
> = 65	110	37.7%	64	22.1%
Sex					<0.001*
Male	199	68.2%	147	50.9%
Female	93	31.8%	142	49.1%
Residence					0.270
Urban	261	89.4%	266	92.0%
Rural	31	10.6%	23	8.0%
Marital Status					0.604
Single	41	14.0%	45	15.6%
Married	251	86.0%	244	84.4%
Living arrangement					0.986
Alone	48	16.4%	49	17.0%
With a partner	8	2.7%	8	2.8%
With a parent/s	236	80.8%	232	80.3%
Income					<0.001*
0–600	51	17.5%	49	17.0%
601–1650	4	1.4%	22	7.6%
1651–3200	34	11.6%	70	24.2%
3201–5250	82	28.1%	79	27.3%
5251–7800	61	20.9%	26	9.0%
7801–10900	50	17.1%	29	10.0%
>10,900	10	3.4%	14	4.8%
Alcohol drinking status					0.336
Yes	65	22.3%	55	19%
No	227	77.7%	234	81%
Smoking status					0.600
Yes	36	12.3%	46	15.9%
No	256	87.7%	243	84.1%
Comorbidity					0.039*
No co-morbidity	141	48.3%	115	39.8%
At least one co morbidity	151	51.7%	174	60.2%
List of co-morbidities	N = 292		N = 289
Diabetic mellitus	84	28.8%	101	34.9%
Hypertension	59	20.2%	90	31.1%
Other	44	15.1%	55	19.0%
Lung diseases	37	12.7%	20	6.9%
Heart diseases	5	1.7%	13	4.5%
HIV AIDS	0	0%	5	1.7%
Cancer	0	0%	1	0.3%
Number of Medication					<0.001
< = 5	118	40.4%	80	27.7%
>5	174	59.6%	209	72.3%

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Abbreviations; N: number (frequency): %: percent

From preexisting medical conditions diabetic mellitus (31.32%) is the most common cause of co morbidity then hypertension (25.6%), other diseases (17%), lung diseases (9.8%), heart diseases (3.09%), HIV (1.7%), and cancer (0.2%) were listed from most to list commonest. Almost three-fourth (72.4%) of malnourished patients on admission were those who took multiple drugs (>5 number of drugs). Greater number patients who took poly medication was seen among malnourished when compared with well nourished (72.3% vs 59.6%, p<0.001)

Nutritional status and clinical outcome among COVID-19 patients

Table 2 shows the nutritional status with their clinical outcome among COVID-19 patients. Larger percentage of ICU admission were seen among malnourished compared with well-nourished (24.2% vs 3.4%, p>0.001). The death analysis result showed that significant difference were seen between malnourished and well-nourished (5.2% vs 0.3%, p<0.001). In addition, Fig 1 shows underweight patients had a larger percentage of ICU admission rate than over weight patients (29.4% vs22.1%, p<0.001).Also the death of underweight patients were higher than over weight patients (7.1% vs 4.4%, p<0.001).

Table 2. Nutritional status with their clinical outcome among COVID-19 patients in SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Out Come (N = 517)	Well nourished		Malnourished		P-value
Out Come (N = 517)	Well nourished		Malnourished		< 0.001*
	Frequency	Percent	Frequency	Percent
ICU	10	3.4%	70	24.2%
Death	1	0.3%	15	5.2%

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Effect of malnutrition on ICU admission and death

During the study period, a total of 16 patient fatalities were recorded, each of whom had been admitted to the Intensive Care Unit (ICU) prior to succumbing. Within this cohort, 15 deaths were observed in the malnourished group, while only one occurred in the well-nourished cohort. Upon further stratification of the malnourished subgroup, the analysis disclosed that 9 individuals were classified as overweight, while 6 were identified as undernourished, all of whom experienced mortality. Table 3 shows multivariable cox regression analysis model fitted to evaluate the effect of malnutrition on ICU admission and death. The Cox proportional hazard model was used for the analysis of nutritional status and COVID-19 adverse outcomes (ICU admission and death). Cox regression Adjusted Hazard Ratio for ICU Admission considering well-nourished as a reference. Show that the rate of ICU admission was significantly associated and were found to be higher among underweight (AHR = 10.02, 95% CI : (8.64–12.10) and overweight (AHR = 7.7, 95% CI : (6.41–9.62) patients when compared with well-nourished ones. But the age, sex, co morbidities and medication variables in the analysis were not significantly associated.

Table 3. Results of Cox proportional hazard analysis on predictor of ICU admission for patients with corona virus disease admitted at SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Variables	Category	Crude HR	Adjusted HR*
Variables	Category	(95% CI)	(95% CI)
Comorbidity
	No co-morbidity	1.22 (0.78,1.9)	1.11(0.70,1.76)
	At least one co-morbidity	1	1
Number of medications
	≤5 medication	1.17(0.73,1.87)	0.94(0.58,1.52)
	>5 medication	1	1
Age Group
	<65 years	0.62(0.32,1.06)	0.79(0.46,1.37)
	≥ 65 years	1	1
Sex
	Male	1.08(0.69,1.69)	0.75(0.47,1.19)
	Female	1	1
Nutritional status
	Well nourished	1	1
	Under weight	9.96(8.14,11.76)	10.02(8.64,12.10)^**
	Over weight	7.29(6.17,9.49)	7.66(6.41,9.62)^**

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** P-value < 0.05

Table 4 shows Cox proportional hazard analysis on predictor of death for COVID patients. The rate of survival probability was significantly associated and were found to be better among well-nourished patients (AHR = 0.06, 95% CI : (0.01–0.44) when compared with malnourished COVID-19 patients.

Table 4. Results of Cox proportional hazard analysis on predictor of death for patients with corona virus disease admitted at SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Variables	Category	Crude HR	Adjusted HR*
Variables	Category	(95% CI)	(95% CI)
Co-morbidity
	No co-morbidity	1	1
	At least one co-morbidity	0.62(0.23,1.69)	0.77(0.28,2.15)
Number of medications
	≤5 medication	1	1
	>5 medication	0.60(0.19,1.88)	0.86(0.27,2.72)
Age Group
	<65 years	1	1
	≥ 65 years	0.96(0.33,2.76)	0.72(0.25,2.10)
Sex
	Male	0.65(0.24,1.72)	1.09(0.40,2.95)
	Female	1	1
Nutritional status
	Well nourished	0.06(0.01,0.44)	0.06(0.01,0.44)**
	Malnourished	1	1

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Fig 2 shows Kaplan-Meir curve comparing ICU admission between nutritional statuses of COVID-19 patients. ICU admission rate of COVID-19 patients was plotted using Kaplan-Meier survival estimates. It shows that there was a statistically significant difference in survival time occurrence of ICU admission between underweight, overweight and well-nourished patients (log rank test P<0.001).

Fig 3 shows Kaplan-Meir curve comparing death between nutritional statuses of COVID-19.The Kaplan Meier survival estimates plotted for the death rate of COVID-19 patients. The result show that there was a statistically significant difference in survival time occurrence of death between malnourished and well-nourished patients (log rank test P<0.001).

Discussion

The aim of the study was to assess the effect of malnutrition on ICU admission and death of COVID-19 patients in SPHMMC, EKGH, and MCCC. As a result, this study suggests that malnutrition increases the risk of ICU admission and death of COVID-19 patients. The study showed that there was a higher ICU admission and death proportion among the malnourished compared with the well-nourished. When compared to well-nourished patients, the rate of ICU admission was significantly associated and found to be higher among underweight [(AHR = 10.02, 95% CI: (8.64–12.10)] and overweight [(AHR = 7.7, 95% CI: (6.41–9.62)] patients. The rate of death was significantly associated with and was found to be lower among well-nourished patients [(AHR = 0.06, 95% CI : (0.01–0.44)] when compared with malnourished patients.

Similar previous studies show malnutrition is often doesn’t gate enough attention due to being under-reported and often poorly documented. Malnutrition in developing countries including Ethiopia tends to refer to children and awareness about adult disease-related malnutrition is lack, so it stills a major problem in a clinical setting. Because of lack of knowledge, routine nutrition screening, assessment and intervention practices are not uniformly done as a part of medical care [23]. This study has tried to address this gap by showing the magnitude of the association between malnutrition and COVID-19 adverse outcomes. In order to recommend mandatory nutritional screening at admission for COVID-19 patients, it is necessary to give appropriate nutritional support.

The analysis of this study showed that being underweight has resulted in a higher rate of ICU admission. The result is consistent with other studies conducted on the effect of underweight on ICU admission. The result of an observational study done in France showed that the prevalence of malnutrition reached 66.7% in patients admitted to ICU [7]. A study done in Italy by Loris et al. in 2020 showed that malnutrition was present in 77% of those admitted to ICU [24]. A retrospective cohort analysis was done in China by enrolling 523 patients. Out of these, 211 individuals (40.3%) admitted to the ICU have a lower BMI [25]. Numerous study conducted in different countries suggests that a higher ICU admission rate was recorded among underweight patients. Due to the lack of malnutrition screening and assessment protocols for COVID-19 patients at admission and inadequate knowledge of health professionals specifically to diagnose malnutrition while at admission.

From the cox regression analysis results, the deaths of well-nourished COVID-19 patients were lower than malnourished patients. A retrospective and single-center study done in China showed that patients with moderate-to-severe malnutrition died at a higher rate than patients with normal or mild malnutrition [26]. The results of two studies conducted in Italy and France showed that well-nourished COVID-19 patients have better survival opportunities than malnourished ones [27, 28]. Therefore, evaluating the nutritional status of COVID_19 patients precisely is helpful for health professionals to identify the disease progression and apply an appropriate therapy strategy in order to reduce the risk of ICU admission and death among malnourished patients.

The nutritional status of the COVID-19 patients at admission is an independent risk factor and has a significant effect on ICU admission and death of COVID-19 patients, which is supported by the literature [7, 10–13, 24, 29]. The study’s findings indicate that being overweight during admission was highly related to ICU admission and death, even when other possible confounders were controlled for, and this finding was consistent with other studies. For example, a study done in Italy showed a higher need for assisted ventilation beyond pure oxygen support and a higher admission to intensive or semi-intensive care units were observed in patients with overweight and obesity even after adjusting for sex, age, and co-morbidities [10].

A retrospective cohort study conducted in the USA showed that severely obese COVID-19 patients (BMI ≥ 35 kg/m2) were 5.39 times more likely to be admitted to ICU when compared to patients with normal weight [12]. Another study done in China on the impact of obesity on disease severity and fatality among patients with COVID-19 showed that obesity raises the risk of hospitalization, ICU admission, invasive mechanical ventilation (IMV) requirements, and death [29].

A retrospective cohort study done in New York, USA showed that patients with overweight and obesity who have COVID-19 are at increased risk for mortality and intubation compared to those with normal BMI [11]. Similarly, a systematic review conducted in the United Kingdom to investigate if patients with obesity are more likely to die from COVID-19 compared to non-obese individuals showed that obesity is a risk factor for the death of COVID-19 patients [13]. Aside from nutritional status, factors such as age, gender, co-morbidities, and medication were not found to be statistically significant in the study’s analysis, but previous research has found an association with worsening COVID-19 outcomes [3, 30–32].

In this study, the data analyzed were genuine anthropometric measurements, or primary data. This study addresses the hidden burden and consequence of malnutrition at admission in COVID-19 patients. As a result, it can be used to persuade hospitals and other healthcare facilities to adopt a strategy and a set of guidelines for identifying patients who may have nutritional risks and the study emphasizes the importance of early nutritional screening and to promote sufficient attention on nutritional intervention through developing guidance for covid-19 nutritional management.

Several limitations should be acknowledged in interpreting this research. The hospital designated for treating COVID-19 patients was government-assigned, and admissions were referrals from various government health facilities, potentially introducing selection bias. Furthermore, as the sole isolation and treatment center in the city with well-equipped intensive care units, there is a risk of bias in patient recruitment. Left truncation or delayed entry may contribute to selection bias as only patients who had not experienced the event were selectively entered at the time of study recruitment. Censoring, akin to loss of follow-up, can impact the study results by yielding incomplete data on participants. Despite efforts to minimize measurement error through rigorous training of data collectors and regular calibration of measurement tools, concerns about measurement accuracy persist.

Conclusions

Malnutrition at the time of admission was shown to increase the risk of ICU admission and mortality among COVID-19 patients. Also, well-nourished COVID-19 patients have better survival opportunities than malnourished patients. To reduce the effects on patients and the healthcare system, it is crucial to assess patients’ nutritional status as soon as they are admitted and to implement the proper nutritional therapy.

Supporting information

S1 File. Data set.

(XLS)

pone.0298215.s001.xls^{(370.5KB, xls)}

S1 Table. Socio-demographic characteristics of COVID-19 patients in SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

(DOCX)

pone.0298215.s002.docx^{(13KB, docx)}

Acknowledgments

We would like to thank Addis Ababa University for facilitating the conduct of the research. We would also like to thank the study participants, the supervisors and data collectors without whom this wouldn’t have been realized.

Data Availability

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

Funding Statement

This study was supported by Addis Ababa University. The funding body had no role in designing the study, in the collection, analysis and interpretation of data, decision to publish, in the writing of this manuscript and in the decision to submit for publication.

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PLoS One. doi: 10.1371/journal.pone.0298215.r001

Decision Letter 0

Wondwossen Amogne Degu

12 Dec 2022

PONE-D-22-22988The Effect of Malnutrition on Adult Covid-19 Patient’s ICU Admission and Mortality in Covid-19 Isolation and Treatment Centers in Ethiopia : A Prospective Cohort StudyPLOS ONE

Dear Dr. Desalegn,

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Dear Yakob Desalegn

Greetings

The paper was reviewed by two independent reviewers and the final decision is that it needs major revision.

Attached below you find the reviewers comments.

(Reviewer 1)

Reviewer Recommendation Term: Major Revision

Rate Review: 0

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Rate Review: 0

Custom Review Question(s): Response

Comments to the Author

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

2. Has the statistical analysis been performed appropriately and rigorously? No

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

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

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) This paper is based on information gathered in Addis Ababa during the COVID-19 epidemic. It tries to find out if malnourished people are more likely to be admitted and more likely to die.

The School of Public Health and the COVID-19 treatment centres gave ethical approval for this study. I don't think these institutions have the power to give ethical approval for a study like this in Ethiopia. Based on what I read on the Addis Ababa University website on December 4th, 2022, I think it should have been the College of Health Sciences and not one of its departments.

The authors say that they were willing to give the data for the study, and they also say that all the data are described in the results. That is not true. For example, we don't know how BMI and MUAC are distributed. I think this paper shouldn't be published until the authors can give full access to all the data they've collected.

The main mistake the authors make is that they call malnutrition both under- and over-nutrition. Also, there is no way to explain how malnutrition was mdefined. It looks like they used both MUAC and BMI to figure if they were malnourshed. But these measures can't be switched around, and the authors don't talk about that.

A well-known risk factor for COVID admission and death is being overweight. But undernutrition is a group of different problems with many causes. It is important for the authors to explain why the patients were undernourished and what medical problems caused them to be thin. The fact that they used more medicines than the other patients suggests that there were underlying causes for their undernutrition.

Does this paper add any new knowledge? Unless the authors can provide much more details about the undernourished group, I do not think so.

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More Reviewer Details

The authors present relevant and unique data on associations between various forms of malnutrition and severe outcomes (ICU and/or death) among patients presenting to a single Ethiopia referral hospital with a Covid-19 diagnosis.

Several major concerns need to be addressed.

1. Malnutrition is a composite category, and the authors need to be clear and consistent. In this paper, both undernutrition and overweight were assessed, but not other kinds of malnutrition (eg micronutrient deficiencies). This is not a problem, but clear definitions need to be given by BMI and/or MAUC, and both types of malnutrition must not be lumped anywhere, when describing associations or assessing risks. These are different populations.

2. BMI>25 and in particular BMI>30 are already well-established risk factors for adverse outcomes following Covid-19 infection. Thus, the added value of this paper is more in showing, or contrasting the risks associated with undernutrition. This would give a more clear perspective to introduction and discussion, and the paper overall.

3. The authors need to present a more thoughtful discussion on implications of their findings, beyond providing unclear statements on 'suitable interventions to be provided'. It is not clear what interventions they want to see implemented, when, why, nor to what extent there are evidence-based interventions, in particular upon hospitalisation, which improve outcomes assessed. Are they suggestion to put obese patients on a strict diet&exercise plan? Force-feeding undernourished patients?

4. Also, it is remarkable that some risk factors observed in other settings (co-morbidities, age) were not significantly associated with adverse outcomes here. This can cast doubt on the validity of the data collected and conclusions reached, and needs at least some thoughtful reflection.

5. In contrast to many other underlying conditions, manifest undernutrition and overnutrition are not very difficult to assess visually by trained staff, thus the statement that these condition are often unrecognised is unclear. Perhaps the authors meant that the associations with of increased risk for adverse outcomes are not always appreciated, but either way it would be good to have references to back up such unexpected claims.

6. More information needs to be given on the Covid-19 policies in the setting. Were these all patients referred, or were they a mix of direct presentations and referrals? Were all patients included because of their Covid-19 related clinical state, or were there also patients included who presented with an unrelated problem, but were routinely tested and found infected?

7. The presentation of data needs to be much improved. Baseline data on BMI and MUAC need to be presented by outcome category. Clarification needs to be given to what extent deaths occurred at ICU or elsewhere, and how data on patients who were first admitted to ICU, and later died (either at ICU or in the ward were analysed. Were there cases where patients opted to go home to die? While the KM curves provide some key information on time to events, it does not clarify patients with both events, and these findings are not properly mentioned in the text. Are there background data on the prevalence of obesity and undernutrition in the catchment populations?

8. The authors also need to reflect on potential non-causal explanations for the association between undernutrition and adverse outcomes. Eg, is it possible that undernourished people have less easy access to health services (eg due to poverty, rural residence, civil unrest, with more time to reach/less available health services, etc), and might they therefore be presenting at a more severe or later stadium, resulting in worse outcomes? Was there information available on distance of the residence to the hospital, and on duration of complaints prior to arrival? How many critically ill (defined as? Assessed by someone indepent from the study team?) were excluded?

9. Please clarify why MUAC was used in addition/as alternative to BMI, as inclusion was restricted to adults, and critically ill patients for whom nutritional status could not be assessed at admission were excluded already?

10. A major overhaul is needed to reduce/eliminate the frequent repetitions and superfluous sentences where no relevant information is presented; also in part of the methods there is an overload of details relating to standard procedures, which is unnecessary; and in parts of the results there is quite a bit of repetition of non-essential data already presented in the tables/figures, rather than highlighting the main findings only in the text.

Some minor comments:

The Abstract mentions in the results that the mean age was 55 years, (16,45) years. Please clarify the 16,45 (also in the Results section).

Methods: it seems a bit odd to justify the measurements referring to a (non-peer reviewed) publication related from the Australian sports federation (ref 14). Is that a relevant context to provide validity to an Ethiopian hospital setting.

Methods: with a mean age of 55, it is surprising that 81% were were categorised as living with parents, and less than 3% with a partner. Presumably, elderly parents might be living with their grown up children, with or without partner? However, in general it is not clear what the added value of this category is, suggest to drop.

Results: unclear why ages were split in only 2 categories, please do at least a sensitivity analysis with eg 4 categories. On the other hand, 7 categories for income do not seem useful, suggest to reduce to eg 4. For smoking and alcohol: clarify if this refers to current situation or ever. The high number of people taking over 5 medications (60%) appears at odds with 48% reporting no co-morbidity, can you explain? More information on what co-morbidities might be helpful.

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Confidential to Editor

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Do you want to get recognition for this review on a Web of Science researcher profile?

Wondwossen Amogne MD, PhD

Academic editor, PLOS ONE

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

Reviewer #1: Partly

Reviewer #2: No

**********

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

Reviewer #1: No

Reviewer #2: No

**********

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

Reviewer #1: No

Reviewer #2: No

**********

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

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Reviewer #1: The authors present relevant and unique data on associations between various forms of malnutrition and severe outcomes (ICU and/or death) among patients presenting to a single Ethiopia referral hospital with a Covid-19 diagnosis.

Several major concerns need to be addressed.

Some minor comments:

The Abstract mentions in the results that the mean age was 55 years, (16,45) years. Please clarify the 16,45 (also in the Results section).

Reviewer #2: This paper is based on information gathered in Addis Ababa during the COVID-19 epidemic. It tries to find out if malnourished people are more likely to be admitted and more likely to die.

Does this paper add any new knowledge? Unless the authors can provide much more details about the undernourished group, I do not think so.

**********

6. 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: Yes: Bernt Lindtjorn

**********

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PLoS One. 2024 Mar 20;19(3):e0298215. doi: 10.1371/journal.pone.0298215.r002

Author response to Decision Letter 0

24 Feb 2023

A rebuttal letter

Dear PLOS ONE's Journal Editor

We would like to express our gratitude for giving us another opportunity to revise the manuscript as per reviewer comments. We affirm that the manuscript has improved a lot from your comments.

We hereby listed Point-by-point responses within the 'Response to Reviewer'.

Kind regards,

'Response to Reviewers

Editor Comments:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Address: Dear reviewer, Thank you for the comment. We have corrected it as recommended. We have checked carefully to ensure the PLOS ONE’S requirements.

2. Thank you for stating the following financial disclosure:

State what role the funders took in the study. If the funders had no role in your study, please state: “The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

Address: Dear reviewer, Thank you for the comment. We have corrected it as recommended (line number 333)

Additional Editor Comments:

Reviewer comments

Several major concerns need to be addressed.

1. Malnutrition is a composite category, and the authors need to be clear and consistent. In this paper, both under nutrition and overweight were assessed, but not other kinds of malnutrition (eg micronutrient deficiencies). This is not a problem, but clear definitions need to be given by BMI and/or MAUC, and both types of malnutrition must not be lumped anywhere, when describing associations or assessing risks. These are different populations.

Address: Dear reviewer, Thank you for the comment. There was clear operational definition for malnutrition using BMI & MUACwith the help of ref [15].Undernourished: COVID-19 case with BMI <18.5 kg/m2and for those critically ill patients who are not capable of measuring BMI the MUAC measurement of<23.7 were considered as underweight (15). Overnourished:COVID-19 case with BMI >25 kg/m2and for those critically ill patients who are not capable of measuring BMI the MUAC measurement of ≥28.1were considered as overweight (15).

Address: Dear reviewer, Thank you for the comment. The add value of this research paper is more on showing the adverse effect of undernourished on COVID-19 patients. As you suggested it is elaborated in detail in discussion section of the paper.

3. The authors need to present a more thoughtful discussion on implications of their findings, beyond providing unclear statements on 'suitable interventions to be provided'. It is not clear what interventions they want to see implemented, when, why, nor to what extent there are evidence-based interventions, in particular upon hospitalization, which improve outcomes assessed. Are they suggestion to put obese patients on a strict diet&exercise plan? Force-feeding undernourished patients?

Address: Dear reviewer, Thank you so much for your valuable comments.The study emphasizes the importance of early nutritional screening and to promote sufficient attention on nutritional intervention through developing guidance for covid-19 nutritional management.

Address: Dear reviewer, Thank you for your insightful comment, our research haven’t revealed any association between co-morbidity and adverse COVID-19 this might be due to the sample size was calculated based on the proportion of adverse effect of COVID-19 patient among the malnourished and well-nourished patients.

Address: Thank you again for your valuable comments. Saying left unrecognized is an ambiguous phrase so we have to change into “doesn’t gate enough attention”this can explain it well. Besides there is a literature which suggests Malnutrition in developing countries including Ethiopia tends to refer to children and awareness about adult disease-related malnutrition is lack, so it stills a major problem in a clinical setting. Because of lack of knowledge, routine nutrition screening, assessment and intervention practices are not uniformly done as a part of medical care (21)

Address: Dear reviewer, Thank you so much for your valuable comments. The centers selected for the study was designated for covid-19 patients treatment only. So the other health institution in the city can referred covid-19 cases to these centers for isolation, well-equipped ICU setup and for their larger admission capacity.But some referred caseswere found while routine covid-19 tests.

7. The presentation of data needs to be much improved. Baseline data on BMI and MUAC need to be presented by outcome category. Clarification needs to be given to what extent deaths occurred at ICU or elsewhere, and how data on patients who were first admitted to ICU, and later died (either at ICU or in the ward were analyzed. Were there cases where patients opted to go home to die? While the KM curves provide some key information on time to events, it does not clarify patients with both events, and these findings are not properly mentioned in the text. Are there background data on the prevalence of obesity and undernutrition in the catchment populations?

Address: Dear reviewer, Thank you so much for your valuable comments.There is no case consideration of ICU admission after death outcome .For further clarification those patients admitted to ICU, and later died while follow up were reported as death outcome.ICU admission and death was censored at 2 months, hence COVID-19 patients who were discharged earlier than follow up time without adverse outcome or stayed in the hospital for more than 2 months were treated as censored observations. There was no any background data on the prevalence of obesity and undernutrition in the catchment population.

Address: Dear reviewer, Thank you so much for your valuable comments. It is bit difficult to reflect some of the factor which may have effect on the association of under nutrition and adverse outcome because of during base line data collection the mentioned factors weren’t included in the study. We didn’t include the onset of the disease because the patients were enrolled based the severity of the disease at admission.Individual who full fill the exclusion criteria they haven’t been registered by the data collectors. So there is no compiled number which shows the number of excluded patients who are critically ill at admission.

Address: Dear reviewer, Thank you so much for the comment. Wrongly wrote the sentence in line 88 as we all know MUAC measurement was need to measure critical ill patients who are not capable of measuring their BMI. So we put the correct sentence 'critically ill patients with both upper extremities amputation were excluded’.MUAC is also recommended for patients with ascites or edema in legs or trunk to gauge dry weight and BMI(16).Other studies confirm that MUAC correlated well with BMI and could be used to identify patients as underweight and overweight(17-20).

Address: Dear reviewer, Thank you for the comments. We agree with you sir. So we have addressed those comments.

Some minor comments:

1. The Abstract mentions in the results that the mean age was 55 years, (16,45) years. Please clarify the 16,45 (also in the Results section).

Address: Dear reviewer, Thank you for the comments. The sentence in line 166 show that the mean age of study sample was 55 years and the standard deviation was 16.45 years.

2. Methods: it seems a bit odd to justify the measurements referring to a (non-peer reviewed) publication related from the Australian sports federation (ref 14). Is that a relevant context to provide validity to an Ethiopian hospital setting.

Address: Dear reviewer, There is no study which shows acceptable TEM level in Ethiopian hospital setting.

3. Methods: with a mean age of 55, it is surprising that 81% were were categorised as living with parents, and less than 3% with a partner. Presumably, elderly parents might be living with their grown up children, with or without partner? However, in general it is not clear what the added value of this category is, suggest to drop.

Address: Dear reviewer, Thank you for the comments. Yes it was confusing, so we have to omit the sentence in line 173-175 general.

4. Results: unclear why ages were split in only 2 categories, please do at least a sensitivity analysis with eg 4 categories. On the other hand, 7 categories for income do not seem useful, suggest to reduce to eg 4.

Address: Dear reviewer, Thank you for the comments. From earlier research adults over 65 years of age represent 80% of hospitalizations and have a 23-fold greater risk of death than those under 65.this is the main reason for age categorization.

5. For smoking and alcohol: clarify if this refers to current situation or ever. The high number of people taking over 5 medications (60%) appears at odds with 48% reporting no co-morbidity, can you explain? More information on what co-morbidities might be helpful.

Address: Dear reviewer, Thank you for the comments. The substance status refers to ever smoking and alcohol drinking status. Even if larger number of covid-19 patients doesn’t have co-morbidity covid-19 disease by itself need supportive medications/supplements like vitamin A, C, zinc and iron as a result they take over 5 medications.

Attachment

Submitted filename: A rebuttal letter Response to reviwers.docx

pone.0298215.s003.docx^{(20.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0298215.r003

Decision Letter 1

Nitai Roy

8 Nov 2023

PONE-D-22-22988R1The Effect of Malnutrition on Adult Covid-19 Patient’s ICU Admission and Mortality in Covid-19 Isolation and Treatment Centers in Ethiopia : A Prospective Cohort StudyPLOS ONE

Dear Dr. Desalegn,

Please submit your revised manuscript by Dec 23 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
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We look forward to receiving your revised manuscript.

Kind regards,

Nitai Roy

Guest Editor

PLOS ONE

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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 #3: (No Response)

Reviewer #4: (No Response)

**********

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

Reviewer #3: Partly

Reviewer #4: Yes

**********

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

Reviewer #3: No

Reviewer #4: No

**********

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

Reviewer #3: No

Reviewer #4: No

**********

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

Reviewer #3: Yes

Reviewer #4: No

**********

6. Review Comments to the Author

Reviewer #3: The authors have conducted a prospective study evaluating the impact of malnutrition on COVID-19 outcomes. The study is important as not many studies have assessed malnutrition in COVID-19, especially in the African continent. Unfortunately the manuscript in its current form has issues with the presentation and interpretation of statistics which need to be addressed before it can be further assessed.

Introduction

1. line 54: Correct "SARS-Cov2 infection (COVID-19) and related pneumonia" to "SARS-CoV-2 infection and related pneumonia (COVID-19)"

Materials and Methods

2. line 85: "All COVID-19 cases with confirmed positive COVID-19 test result were included..." What type of SARS-CoV-2 tests were used, eg. RT-PCR or antigen? The line should read SARS-CoV-2 test rather than COVID-19 test. How were patients with SARS-CoV-2 infection differentiated from those who specifically had COVID-19 pneumonia?

3. line 86: "Patients were excluded from the study if they are critically with both upper extremities amputation ill..." Correct this sentence for meaning.

4. line 99: "Based on this assumption, a total of 581 COVID-19 patients were included in the study based on the highest sample size obtained from ICU admission prevalence." As a prospective study, it would be better to state that 581 COVID-19 patients were required rather than included.

5. line 128: "MUAC measurement of<23.7 were considered" Provide unit of measurement for MUAC each time it is used.

Results

6. line 177: "There were statistically significant association seen between age, sex, income, co morbidity,

medication and nutritional status." This sentence could be more specific, eg. "There were statistically significant association seen between nutritional status and younger age, female sex, income, co morbidity, and higher number of medication."

7. line 178: "More percentage of patients < 65 years age group was seen in both malnourished (77.9%) and well-nourished 182 (62.3%) nutritional status." This statement is non-contributory as it only means more patients aged < 65 we recruited compared to > 65. It would be more helpful to give the percentage of patients under and over 65 who were well nourished and malnourished (ie. use the number of patients in each age group as the denominator) to enable comparisons.

8. line 179: "Malnutrition of male patients was higher than female in the cohort (50.9% vs 49.1%, p<0.001)." It is true that the absolute number of male patients who were malnourished is higher, but the proportion of female patients who are malnourished is actually higher (42% vs 60%). The authors have again used the incorrect denominator. The same is true for the other comparisons made in this paragraph.

9. line 184: "From preexisting medical conditions diabetic mellitus (31.32%) is the most common cause of co

morbidity then hypertension (25.6%), other diseases (17%), lung diseases (9.8%)..." Consider including specific comorbidities in table 1, divided by nutritional status. Several of these comorbidities are known to be risk factors for poor outcomes in COVID-19 and their effects on the findings of this study need to be considered.

10. line 195: "Nutritional status and clinical outcome among COVID-19 Patients". It is difficult to assess the accuracy of the statistics provided here as no absolute numbers are provided. It is also not stated how many patients were over weight. Over weight patients seem to be separated in some places, and grouped together with well-nourished patients in others, such as the preceding paragraphs and table 1. The authors need to be more consistent with how patients are grouped.

Discussion

11. line 263: "Of these, 211(40.3%) were admitted to the ICU as a result of lower BMI(23)." More accurate to state that they had lower BMI, not that the admission was as a result of the lower BMI.

12. line 264: "Every study conducted in different". Numerous studies, but not every study. Many studies did not report underweight patients.

Other

13. Figure 1: The percentages only add up to 66.68%. What happened to the remainder?

Reviewer #4: This manuscript reports the findings of a multi-center observational study of assessing the effect of malnutrition on Covid-19 outcome at Covid centers in Ethiopia. The manuscript is generally well written, and data of this type remain very unusual for Africa despite major concerns were detected on the methods and analysis. I hope the authors will find the following comments helpful.

The introduction needs some minor language editing.

Study design: The authors dictated they have conducted a prospective cohort despite this unusually they have 0% drop out. Furthermore, the authors follow the patients until 2 months since the follow-up seems long there was no withdrawal which is impractical.

Since the study was prospective cohort all 581 patents were followed for 2 months but was there a patient died being admitted to ICU? The authors should perform a sub-group analysis for malnutrition and death based on ICU admission status.

How many patients were admitted to ICU and died during the follow-up? How the authors treat those patients defer admission to ICU but decided to be admitted by the attending physician?

Why patients were followed for 2 months? Was there any justification?

The authors should cite paper for the auumption used for double population formula.

Statistical analysis: A survival analysis model was used to determine whether there was association between malnutrition and adverse Covid-19 outcome. However proper statistical procedures were not employed. The dependent variable should be time to event (event= admission to ICU or death). I strongly Did the authors check the proportional hazard assumption (using Schonefeld residual or log-log plot) before fitting the regression model? If so, it should have to be reported. The authors also chel the dataset they used fit for the model they fit (use Co-Snell or Martingale residuals). Was the Log-rank test significant for other variables which were adjusted in the multivariable model since if the variable is not significant on the Log-rank test it is not necessary to include in the Cox regression model. A sensitivity analysis should be performed sine the events were few.

Results: the results were a little confusing the authors should present 1. The baseline characteristics 2. The exposure and outcome tabulation 3. The regression model results. The figures showing the Kaplan-Meier survival curve are not visible. The result of Cox-regression model ton assesses the effect of malnutrition on Covid-19 mortality shows there was no difference between the crude and adjusted hazard ratios this indicate the variable (Malnourished/ Well-nourished) was not confounded by other variables this indicated the baseline characteristics were similar which is unlikely and results to be very questionable.

Discussion: This may be clearer to the reader if it adhered more closely to a standard four paragraph structure: Para 1: summaries main findings, Para 2: compare and contrast with previous work, Para 3: strengths and limitations, Para 4: conclusions and future work. The authors cover most of this already but I think the limitations could be more detailed.

Thanks for the opportunity to review this paper.

**********

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 #3: No

Reviewer #4: No

**********

PLoS One. 2024 Mar 20;19(3):e0298215. doi: 10.1371/journal.pone.0298215.r004

Author response to Decision Letter 1

5 Jan 2024

A rebuttal letter

Dear PLOS ONE's Journal Editor

We would like to express our gratitude for giving us another opportunity to revise the manuscript as per reviewer comments. We affirm that the manuscript has improved a lot from your comments.

We hereby listed Point-by-point responses within the 'Response to Reviewer'.

Kind regards,

*N.B, the line number we mentioned here is based on the revised track change manuscript file

'Response to Reviewers

Reviewer comments

Reviewer #3:

1. Line 54: Correct "SARS-Cov2 infection (COVID-19) and related pneumonia" to "SARS-CoV-2 infection and related pneumonia (COVID-19)"

Address: Dear reviewer, Thank you for your valuable suggestion. We have duly revised the statement in accordance with your guidance. (Page 3, line number 54).

2. Line 85: "All COVID-19 cases with confirmed positive COVID-19 test result were included..." What type of SARS-CoV-2 tests were used, eg. RT-PCR or antigen? The line should read SARS-CoV-2 test rather than COVID-19 test. How were patients with SARS-CoV-2 infection differentiated from those who specifically had COVID-19 pneumonia?

Address: Thank you for bringing this matter to our attention in the main research paper we have addressed the issue by explicitly stating that we utilized the rRT-PCR test for diagnosing the SARS-CoV-2 virus. This clarification has also been incorporated into the track change document for your reference. (Page 5, line number 89-91).

3. Line 86: "Patients were excluded from the study if they are critically with both upper extremities amputation ill..." Correct this sentence for meaning.

Address: Dear reviewer, Thank you for your valuable suggestion. We have revised the sentences to enhance clarity and meaning, aligning with your feedback (page 5, line number 92-94)

4. Line 99: "Based on this assumption, a total of 581 COVID-19 patients were included in the study based on the highest sample size obtained from ICU admission prevalence." As a prospective study, it would be better to state that 581 COVID-19 patients were required rather than included.

Address: Thank you for your valuable suggestion. In response, we have refined the statement to convey a more accurate representation. Instead of stating that 581 COVID-19 patients were included, we now specify that this number was required for the study based on the highest sample size obtained from ICU admission prevalence (page 5, line number 105)

5. Line 128: "MUAC measurement of<23.7 were considered" Provide unit of measurement for MUAC each time it is used.

Address: Thank you for bringing this matter to our attention. We have addressed the issue, and the corrected measurement is now recorded as 23.7 cm. (page 6, line number 134)

6. Line 177: "There were statistically significant association seen between age, sex, income, co morbidity, medication and nutritional status." This sentence could be more specific, e.g. "There were statistically significant association seen between nutritional status and younger age, female sex, income, co morbidity, and higher number of medications."

Address: Dear reviewer, Thank you for your suggestion. We have refined the sentence to specify a statistically significant association between nutritional status and younger age, female sex, income, co-morbidity, and a higher number of medications (page 9, line number 191-192).

7. Line 178: "More percentage of patients < 65 years age group was seen in both malnourished (77.9%) and well-nourished 182 (62.3%) nutritional status." This statement is non-contributory as it only means more patients aged < 65 we recruited compared to > 65. It would be more helpful to give the percentage of patients under and over 65 who were well nourished and malnourished (i.e. use the number of patients in each age group as the denominator) to enable comparisons.

Address: Thank you for your constructive comment. In response, we have made the suggested revision and incorporated it with track changes for your review. (Page 9, line number 192-195).

8. Line 179: "Malnutrition of male patients was higher than female in the cohort (50.9% vs 49.1%, p<0.001)." It is true that the absolute number of male patients who were malnourished is higher, but the proportion of female patients who are malnourished is actually higher (42% vs 60%). The authors have again used the incorrect denominator. The same is true for the other comparisons made in this paragraph.

Address: Thank you for highlighting this issue. We have addressed it by making the recommended revision, and you can find the changes incorporated in the track changes for your review (Page 9, line number 196-197).

9. Line 184: "From preexisting medical conditions diabetic mellitus (31.32%) is the most common cause of co-morbidity then hypertension (25.6%), other diseases (17%), lung diseases (9.8%)..." Consider including specific comorbidities in table 1, divided by nutritional status. Several of these comorbidities are known to be risk factors for poor outcomes in COVID-19 and their effects on the findings of this study need to be considered.

Address: Dear reviewer, Thank you for your comment. We have addressed the feedback by incorporating the listed co-morbidities into Table 1 (Page 11-12).

10. Line 195: "Nutritional status and clinical outcome among COVID-19 Patients". It is difficult to assess the accuracy of the statistics provided here as no absolute numbers are provided. It is also not stated how many patients were overweight. Overweight patients seem to be separated in some places, and grouped together with well-nourished patients in others, such as the preceding paragraphs and table 1. The authors need to be more consistent with how patients are grouped.

Address: Thank you for your comment. The absolute numbers of both malnourished and well-nourished cases have been specified as recommended (Page 9, line number 189-190).

11. Line 263: "Of these, 211(40.3%) were admitted to the ICU as a result of lower BMI (23)." More accurate to state that they had lower BMI, not that the admission was as a result of the lower BMI.

Address: Dear reviewer, Thank you for your comment. We have rephrased the statement according your suggestion. (Page 18, line number 291-292).

12. Line 264: "Every study conducted in different". Numerous studies, but not every study. Many studies did not report underweight patients.

Address: Dear reviewer, your comment is absolutely right and we have corrected it as recommended (Page 18, line number 293-294).

13. Figure 1: The percentages only add up to 66.68%. What happened to the remainder?

Address: Dear reviewer, figure 1 only shows the percentage of ICU admission and death among malnourished and well-nourished patients, out of the total COVID-19 patients enrolled. The remaining were neither admitted to the ICU nor died. (The figure only shows patients with an outcome)

Reviewer #4

1. The introduction needs some minor language editing.

Address: Dear reviewer, we have revised the whole manuscript for spelling and grammar.

2. The authors dictated they have conducted a prospective cohort despite this unusually they have 0% drop out. Furthermore, the authors follow the patients until 2 months since the follow-up seems long there was no withdrawal which is impractical.

Address: Dear reviewer, your comment is absolutely right and we understand your concern. During the initial days of the COVID-19 pandemic in Ethiopia, a limited number of isolation and treatment centers were available, wherein patients underwent isolation for several weeks. The isolation duration extended until two consecutive rRT-PCR tests returned negative results, at which point patients were considered cleared. This regulatory requirement compelled patients to remain in isolation until the manifestation of the desired outcome. Consequently, there was no challenge in maintaining follow-up within our cohort.

3. Since the study was prospective cohort all 581 patents were followed for 2 months but was there a patient died being admitted to ICU? The authors should perform a sub-group analysis for malnutrition and death based on ICU admission status.

Address: Thank you for your insightful comment, we did not include cases where ICU admission occurred after the patient's demise in our analysis of death outcomes. For additional clarification, instances involving patients admitted to the ICU who subsequently passed away during the follow-up period were classified and reported as death outcomes.

4. How many patients were admitted to ICU and died during the follow-up? How the authors treat those patients defer admission to ICU but decided to be admitted by the attending physician? Why patients were followed for 2 months? Was there any justification?

Address: Dear reviewer, Thank you for identifying this flow. In our efforts to rectify the issue, we have revised the statement to provide a clearer depiction of the overall mortality and subcategories based on nutritional status. The total number of deaths recorded during the study period was 16, comprising 9 individuals classified as overweight, 6 identified as undernourished, and 1 classified as well-nourished. Notably, all patients who succumbed had been admitted to the Intensive Care Unit (ICU) prior to their demise.

This period allows for monitoring the protracted recovery period and identifying persistent symptoms like fatigue and neurological issues. Additionally, it enables the assessment of severe outcomes such death and ICU admission. On top of this period allows us to identify long-term complications, including those emerging weeks after recovery.

5. The authors should cite paper for the assumption used for double population formula.

Address: Dear reviewer, the assumptions regarding sample size were initially included in the main document, but for manuscript purposes, they were omitted. However, in the current version, these assumptions have been reintroduced and appropriately cited. The basis for determining the prevalence of ICU admission and death among COVID-19 patients was derived from articles 31 and 32 (Page 24, line number 454-458).

6. Statistical analysis: A survival analysis model was used to determine whether there was association between malnutrition and adverse Covid-19 outcome. However proper statistical procedures were not employed. The dependent variable should be time to event (event= admission to ICU or death). I strongly Did the authors check the proportional hazard assumption (using Schonefeld residual or log-log plot) before fitting the regression model? If so, it should have to be reported.

Address: Dear reviewer, Thank you for your insightful comment. In our analysis conducted using SPSS software, both Schoenfeld residuals and log-log plots were employed to assess the proportional hazard assumption, specifically examining the parallelism between survival curves for the variables of interest. This comprehensive evaluation aimed to ensure the constancy of hazard ratios over time. The results of these diagnostic checks are duly reported in our study as recommended (Page 8, line number 159-161).

7. The results were a little confusing the authors should present 1. The baseline characteristics 2. The exposure and outcome tabulation 3. The regression model results. The figures showing the Kaplan-Meier survival curve are not visible.

Address: Thank you for your valuable feedback. While the baseline characteristics and regression details were initially lacking elaboration in the manuscript, I want to highlight that the exposure and outcome tabulations, which were previously omitted, have now been incorporated in the latest version. This addition enhances the comprehensiveness of the manuscript and addresses the noted omission.

8. The result of Cox-regression model ton assesses the effect of malnutrition on Covid-19 mortality shows there was no difference between the crude and adjusted hazard ratios this indicate the variable (Malnourished/ Well-nourished) was not confounded by other variables this indicated the baseline characteristics were similar which is unlikely and results to be very questionable.

Address: Dear reviewer, Thank you for your valuable comment. The observed similarity in the crude and adjusted hazard ratios concerning death reports among well-nourished and malnourished patients can be attributed to the limited number of recorded deaths during the study period. Specifically, the occurrence of only one death within the well-nourished cohort may have led to statistical challenges and influenced the stability of hazard ratio estimates.

9. This may be clearer to the reader if it adhered more closely to a standard four paragraph structure: Para 1: summaries main findings, Para 2: compare and contrast with previous work, Para 3: strengths and limitations, Para 4: conclusions and future work. The authors cover most of this already but I think the limitations could be more detailed.

Address: Dear reviewer, Thank you for your valuable input. In response to your guidance, we have made efforts to concisely address the limitations within the discussion section, adhering to the goal of brevity as suggested.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0298215.s004.docx^{(20.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0298215.r005

Decision Letter 2

Nitai Roy

22 Jan 2024

The Effect of Malnutrition on Adult Covid-19 Patient’s ICU Admission and Mortality in Covid-19 Isolation and Treatment Centers in Ethiopia : A Prospective Cohort Study

PONE-D-22-22988R2

Dear Dr. Desalegn,

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

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

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

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

Kind regards,

Nitai Roy

Guest Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #4: All comments have been addressed

Reviewer #5: (No Response)

**********

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Reviewer #4: Authors tried their best to amend the manuscript based on the comments provided, I would like to appreciate that. The only minor concern I have is that the authors must cite a literature where did they get the assumption for sample size calculation “……P1(Proportion of ICU admission among malnourished 96 patients) =9.5%, P2(Proportion of ICU admission among well-nourished patients) =9.5%.also P1 97 and P2 assumed for death P1 (Proportion of death among malnourished patients)=1.5%,P2 98 (Proportion of death among well-nourished patients)=1.5% ,Z α/2 =1.96 at a 95% level of 99 confidence, 5% margin of error…….”. Merit the publication of this manuscript given several limitations outlined since studies in critical care outcome is rare in the low-resource setting like Ethiopia I don’t have further questions and I would like to congratulate the authors for doing this work.

Reviewer #5: (No Response)

**********

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 #4: Yes: Amanuel Sisay Endeshaw

Reviewer #5: Yes: Md Ekhtear Hossain

**********

PLoS One. doi: 10.1371/journal.pone.0298215.r006

Acceptance letter

Nitai Roy

8 Mar 2024

PONE-D-22-22988R2

PLOS ONE

Dear Dr. Nigatu,

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

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

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

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on behalf of

Dr. Nitai Roy

Guest 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. Data set.

(XLS)

pone.0298215.s001.xls^{(370.5KB, xls)}

S1 Table. Socio-demographic characteristics of COVID-19 patients in SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

(DOCX)

pone.0298215.s002.docx^{(13KB, docx)}

Attachment

Submitted filename: A rebuttal letter Response to reviwers.docx

pone.0298215.s003.docx^{(20.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0298215.s004.docx^{(20.7KB, docx)}

Data Availability Statement

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

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PERMALINK

The effect of malnutrition on adult Covid-19 patient’s ICU admission and mortality in Covid-19 isolation and treatment centers in Ethiopia: A prospective cohort study

Lencho Mekonnen Jima

Gudina Egeta Atomsa

Johane P Allard

Yakob Desalegn Nigatu

Roles

Abstract

Background

Method

Results

Conclusion

Introduction

Materials and methods

Study setting

Study design

Eligibility criteria

Sample size calculation

Sampling procedures

Data collection

Statistical analysis

Ethical consideration

Result

Socio-demographics characteristics

Admission-related clinical characteristics of the patients

Table 1. General admission-related characteristics of the patients according to nutrition status at baseline among COVID-19 patients in SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Nutritional status and clinical outcome among COVID-19 patients

Table 2. Nutritional status with their clinical outcome among COVID-19 patients in SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Fig 1. Nutritional status with their clinical outcome among COVID-19 patients in SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Effect of malnutrition on ICU admission and death

Table 3. Results of Cox proportional hazard analysis on predictor of ICU admission for patients with corona virus disease admitted at SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Table 4. Results of Cox proportional hazard analysis on predictor of death for patients with corona virus disease admitted at SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Fig 2. Kaplan-Meir curve comparing ICU admission between nutritional statuses of COVID-19 patients (p < 0.01) admitted at SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Fig 3. Kaplan-Meir curve comparing death between nutritional statuses of COVID-19 patients (p < 0.01) admitted at SPHMMC, EKGH and MCCC, Addis Ababa, 2021.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Wondwossen Amogne Degu

Roles

Author response to Decision Letter 0

Decision Letter 1

Nitai Roy

Roles

Author response to Decision Letter 1

Decision Letter 2

Nitai Roy

Roles

Acceptance letter

Nitai Roy

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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