Covid-19 mortality risk factors:  Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia

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The clinical features of this illness range from asymptomatic illness to death. Aim To analyse the survival of patients admitted to a Tunisian tertiary care center (Sahloul University Hospital) and identify independent predictive factors for hospital COVID-19 mortality. Methods A longitudinal study was conducted among confirmed COVID-19 patients hospitalized in Sahloul University Hospital between September 2020 and September 2022. Cox univariate regression was used to calculate the Hazard Ratio (HR) of death for patient characteristics regarding the time at risk. Risk factors with a p value of 0.2 or less in the univariate analysis were initially included in the multivariate Cox regression models. Results During the study at Sahloul Hospital, 1978 patients were hospitalized, averaging 55.65 years in age (± 21.39). Among them, 417 deaths occurred, with a median survival of 30 days (± 3.11) [23.904-36.096]. Factors significantly affecting the survival curve were: Age, male gender, comorbidities, diabetes, hypertension, endocrine diseases, obesity, intubation, and ICU admission Multilevel survival analyses revealed that hypertension (aHR 1.24; CI [1.02-1.52]; p=0.028), ICU admission (aHR 12.20- CI [9.56-15.57]; p<10-3), and male gender (HR 1.19; CI [0.98-1.46]; p=0.076) were independent factors associated with COVID-19 mortality. Conclusion Our study concluded the importance of the early identification of high-risk COVID-19 patients to decrease this virus mortality. A good understanding of the possible clinical factors associated with COVID-19 severity is helpful for clinicians in identifying patients who are at high risk and require prioritized treatment to minimize death. 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F1000Research 2024, 13 :1215 ( https://doi.org/10.12688/f1000research.156234.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia [version 1; peer review: 1 approved with reservations] ARWA NEFFATI https://orcid.org/0000-0003-0067-274X 1 , Hela Ghali https://orcid.org/0000-0001-5330-9069 1-3 , Rania Bannour https://orcid.org/0000-0001-9363-4536 4 , [...] Sana bhiri 1,3 , Habiba Ben hamada 2,3,5 , Balsam Kacem 6 , Mohamed Ben Rejeb 1-3 , Asma Ben Cheikh 1-3 , Mohamed Kahloul 2,3,5 , Walid Naija 2,3,5 , Halim Trabelsi 7 , Houyem Said Laatiri 1-3 ARWA NEFFATI https://orcid.org/0000-0003-0067-274X 1 , Hela Ghali https://orcid.org/0000-0001-5330-9069 1-3 , [...] Rania Bannour https://orcid.org/0000-0001-9363-4536 4 , Sana bhiri 1,3 , Habiba Ben hamada 2,3,5 , Balsam Kacem 6 , Mohamed Ben Rejeb 1-3 , Asma Ben Cheikh 1-3 , Mohamed Kahloul 2,3,5 , Walid Naija 2,3,5 , Halim Trabelsi 7 , Houyem Said Laatiri 1-3 PUBLISHED 11 Oct 2024 Author details Author details 1 Department of Prevention and Security of Care, Sahloul University Hospital,, Sousse, Tunisia 2 LR20SP06, Sahloul University Hospital, Sousse, Tunisia 3 Faculty of Medicine of Sousse, University of Sousse, Sousse, Tunisia 4 Faculty of Medicine of Sousse, Department of Family Medicine, Research Laboratory LR12ES03, University of Sousse, Sousse, Tunisia 5 Department of Surgical Intensive Care, Sahloul University Hospital, Sousse, Tunisia 6 Department of Pharmacy, Faculty of Pharmacy of Monastir, University of Monastir, Tunisia, Sahloul University Hospital, Monastir, Tunisia 7 Laboratory of Microbiology, Sahloul University Hospital, Sousse, Tunisia ARWA NEFFATI Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation Hela Ghali Roles: Conceptualization, Data Curation, Investigation, Methodology, Software, Supervision, Writing – Review & Editing Rania Bannour Roles: Data Curation, Investigation, Software, Writing – Review & Editing Sana bhiri Roles: Visualization Habiba Ben hamada Roles: Visualization Balsam Kacem Roles: Visualization Mohamed Ben Rejeb Roles: Validation, Writing – Review & Editing Asma Ben Cheikh Roles: Visualization Mohamed Kahloul Roles: Project Administration, Supervision Walid Naija Roles: Project Administration, Supervision Halim Trabelsi Roles: Project Administration, Supervision Houyem Said Laatiri Roles: Project Administration, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Tunisia was one of the most affected nations with COVID-19 disease. The clinical features of this illness range from asymptomatic illness to death. Aim To analyse the survival of patients admitted to a Tunisian tertiary care center (Sahloul University Hospital) and identify independent predictive factors for hospital COVID-19 mortality. Methods A longitudinal study was conducted among confirmed COVID-19 patients hospitalized in Sahloul University Hospital between September 2020 and September 2022. Cox univariate regression was used to calculate the Hazard Ratio (HR) of death for patient characteristics regarding the time at risk. Risk factors with a p value of 0.2 or less in the univariate analysis were initially included in the multivariate Cox regression models. Results During the study at Sahloul Hospital, 1978 patients were hospitalized, averaging 55.65 years in age (± 21.39). Among them, 417 deaths occurred, with a median survival of 30 days (± 3.11) [23.904-36.096]. Factors significantly affecting the survival curve were: Age, male gender, comorbidities, diabetes, hypertension, endocrine diseases, obesity, intubation, and ICU admission Multilevel survival analyses revealed that hypertension (aHR 1.24; CI [1.02-1.52]; p=0.028), ICU admission (aHR 12.20- CI [9.56-15.57]; p<10-3), and male gender (HR 1.19; CI [0.98-1.46]; p=0.076) were independent factors associated with COVID-19 mortality. Conclusion Our study concluded the importance of the early identification of high-risk COVID-19 patients to decrease this virus mortality. A good understanding of the possible clinical factors associated with COVID-19 severity is helpful for clinicians in identifying patients who are at high risk and require prioritized treatment to minimize death. READ ALL READ LESS Keywords COVID-19; Mortality, predictive factors, Survival analysis Corresponding Author(s) ARWA NEFFATI ( [email protected] ) Close Corresponding author: ARWA NEFFATI Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 NEFFATI A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: NEFFATI A, Ghali H, Bannour R et al. Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :1215 ( https://doi.org/10.12688/f1000research.156234.1 ) First published: 11 Oct 2024, 13 :1215 ( https://doi.org/10.12688/f1000research.156234.1 ) Latest published: 11 Oct 2024, 13 :1215 ( https://doi.org/10.12688/f1000research.156234.1 ) Introduction The coronavirus disease SARS-CoV-2 (COVID-19) was reported for the first time in China (Wuhan) in December 2019. After a few months, it became a global emergency, as more than 245 million positive cases were affected and 45 million deaths were caused worldwide, with a mortality rate of approximately 2%. 1 This disease was classified as a pandemic by the World Health Organization (WHO) in March 2020. Tunisia is one of the most affected countries with a high rate of patients with acute respiratory syndrome coronavirus. In fact, 1145686 cases and 29249 deaths were reported as of September 2022. 2 The clinical features of COVID-19 range from asymptomatic to critical illness and death. Some cases may experience respiratory failure that requires mechanical ventilation and organ failure requiring admission to the intensive care unit (ICU). A literature review in 2020 showed that clinical predictors of disease severity ranged from demographic factors (such as ‘older age’), comorbidities (such as cardiovascular disease, chronic kidney disease, chronic lung diseases (particularly COPD), diabetes mellitus, hypertension, immunosuppression and obesity) and severe hypoxemia. 3 A good understanding of these possible clinical factors associated with COVID-19 severity is helpful for clinicians in identifying patients who are at high risk and require prioritized treatment to prevent disease progression and adverse outcomes. Thus, this study was conducted to identify independent predictive factors for hospital mortality of COVID-19 patients in a Tunisian tertiary care center from September 2020 to September 2022. Methods Study design A longitudinal exhaustive study was conducted among COVID-19 patients in a tertiary care center (Sahloul University Hospital) from September 2020 to September 2022. Settings Sahloul University Hospital is a 690-bed tertiary care hospital in Sousse-Tunisia according to 2020 data. It is composed of seven medical departments, eight surgical departments, seven intensive care units, a developed technical platform, and an emergency department. It also includes radiology, nuclear medicine, prevention and safety of care department and medical analysis laboratories (hematology, anatomopathological-labs, biochemistry, and microbiology). Since the declaration of the first case of SARS-Cov2 infection in Tunisia, on March 4th, 2020, a COVID-19 cell was created in Sahloul University Hospital to undertake anticipatory measures. Many activities were implemented, including an organization of the COVID-19 circuit including installation of an outdoor presorting station near the emergency room and at the hospital entrance, setting up a COVID-19 unit in the emergency room, opening the transplant unit for the hospitalization of COVID-19 patients and the definition of the inpatient circuit. The second activity included programming theoretical and practical training sessions to master and respect the rules of hospital hygiene and the measures of protection against the risk of contamination by SARS-COV2. Indeed, the Prevention and Safety of Care Department organized training sessions on good hand hygiene practices; standard precautions; prevention of transmission of COVID-19 infection; dressing and undressing practices; and biological cleaning of COVID-19 patient isolation units. Other important activities were implemented during the pandemic, such as the drafting of guides, procedures and protocols, and audits and assessments of diverse activities among health workers. Continuous daily monitoring of the epidemiological status of COVID-19 patients was performed using a preestablished grid. Monthly reports were written and distributed to the medical committee describing the epidemic. There was regular feedback to the medical committee and the regional health department. To optimize management of the covid-19 pandemic in our hospital, hospitalization services have been reorganized into three main structures, each adapted to a particular level of severity: 1- Conventional hospitalization units: these units have simple oxygen therapy facilities providing a maximum flow rate of < 15 l/min for patients with parenchymal damage < 75%. 2- Intermediate structures: these structures correspond to conventional hospitalization services, reinforced by respiratory assistance supports such as CPAP, and Optiflow. These facilities care for patients with less than 75% parenchymal involvement, and whose needs correspond to a FiO 2 < 60% and a PEEP 75%; or high oxygen requirements (FiO 2 >60% or PEEP > 6), or an associated complication (stroke, acute myocardial ischemia, myocarditis, acute renal failure requiring bypass surgery, pulmonary embolism). Study population Our study population included confirmed COVID-19 patients hospitalized in Sahloul University Hospital from September 2020 to September 2022. Non inclusion criteria: Non hospitalized patients in Sahloul University Hospital during the study period. Data collection A database including COVID-19 monitoring cases since the hospitalization of the first confirmed COVID-19 case in September 2020 has been regularly updated in the ‘Prevention and Safety of Care Department’ of Sahloul University Hospital of Sousse, Tunisia. All new cases of COVID_19 hospitalized in the various units have been registered. Daily monitoring of the evolution of the patients’ conditions was provided by the hygienist technicians of the department. The data included information on age, genre, governorate, delegation, comorbidities, computed tomography data (CT), polymerase chain reaction (PCR), rapid diagnostic tests (RDT), oxygen source, tracheotomy, noninvasive ventilation (NIV), intubation, entry date, evolution, date of evolution, length of stay, exit location, discharge service, vaccination, dates of the different doses of vaccination, date of onset of symptoms, and vaccine type. COVID-19 vaccination data were collected as a 5-category variable: Registered; Not registered; Vaccinated 1 shot; Vaccinated 2 shots; and Vaccinated 3 shots and were dichotomized into vaccinated and not vaccinated. Variable definition - A confirmed COVID-19 case was defined as a patient with laboratory confirmation of COVID-19 infection, irrespective of clinical signs and symptoms. - Compatible clinical symptoms, an epidemiological link and radiological proof when having a nonreactive serological test for COVID-19. The result of tomography chest scanning was classified by radiologists as being compatible with COVID-19, uncertain, showing a noninfectious pattern or a normal pattern. - Length of stay: The period between the date of admission and the exit date (calculated in days). - Patients were admitted to the ICU if they presented an acute respiratory distress syndrome defined as hypoxemia secondary to a rapid onset of noncardiogenic pulmonary edema with hypoxemia with a PaO 2 /FiO 2 ratio of less than 300 mg on positive end-expiratory pressure (PEP) or continuous positive airway pressure. 4 - Critical COVID-19 illness was defined as Vital distress, shock, sepsis and/or organ failure and/or the need for invasive or non-invasive respiratory assistance. 5 - Chronic kidney disease was defined as “abnormalities of kidney function or structure present for more than 3 months or an estimated glomerular filtration rate (eGFR) of less than 60 ml/min/1.73 m 2 on at least 2 occasions 90 days apart (with or without markers of kidney damage). 6 - Cardiovascular diseases are all conditions affecting the heart or blood vessels, including strokes, coronary heart disease, peripheral arterial diseases, heart valve disease, and aortic disease. 7 - Endocrine diseases include all disorders related to the endocrine glands of the body, such as dysthyroid, acromegaly, Addison’s disease, and Cushing’s syndrome. 8 - Neurological disabilities include a wide range of disorders, such as epilepsy, learning disabilities, neuromuscular disorders, ADD, brain tumor, cerebral palsy … 9 - Obesity was defined as a body mass index (BMI)≥30 kg/m 2 . 10 Several types of COVID-19 vaccines have been developed, including inactivated or weakened virus vaccines ( Sinopharm COVID-19 vaccine : May 7, 2021; and Sinovac-CoronaVac vaccine : June 1, 2021), protein-based vaccines ( The Bharat Biotech BBV152 COVAXIN vaccine : November 3, 2021; The Covovax (NVX-CoV2373) vaccine : December 17, 2021), DNA vaccines (AstraZeneca/Oxford: February 16, 2021, Johnson vaccine March 12, 2021) and (Sputnik V), nucleic acid (mRNA) vaccines: Pfizer/BioNTech (December 31, 2020) and Moderna April 30, 2021. 11 Data analysis All statistical analyses were performed using the Statistical Package for Social Sciences SPSS (version 23, SPSS Inc., Chicago, USA). Normality of quantitative variables was assessed using Kolmogrov Smirnov test. Normally distributed variables were described using means and standard deviation and non normal variables were presented using the median and the interquartile range (IQR) [25th percentile-75th percentile]. Categorial variables were described using the number of cases and percentages. Survival function was graphically represented by Kaplan-Meier curve. Cox univariate regression was used to calculate the Hazard Ratio (HR) of death for patient characteristics regarding the time at risk. Risk factors with a p value of 0.2 or less in the univariate analysis were initially included in the multivariate Cox regression models. For all statistical tests, statistical significance was defined as p<0.05. Ethical considerations The present Study was reviewed and approved by the ‘ethics and research committee of Sahloul University Hospital and registered under the IRB approval number HS 58-2020 on 9 th July 2020. The committee is an academic body responsible for ensuring the ethical integrity of research conducted within the institution. This study was conducted with respect of the principles of the research ethics including patients’ anonymity and data confidentiality. Data was collected entirely from patient records, approval was obtained from the records holder to access and use the data. This ensures that all data used in the study complies with ethical standards and respects patient confidentiality and autonomy. The consent from participants was waived by the ethical approval committee. Results Description of the study population Overall, we included 1978 confirmed COVID-19 cases from 26 September 2020 to 30 September 2022 with a sex ratio (H/F) of 1.2. The mean age and the median length of stay were 55.65±21.39 years [1 – 94] and 8 days, 4 – 9 , 11 – 14 respectively. The most common comorbidity among the population was hypertension (42.7%). The different characteristics of hospitalized patients are summarized in Table 1 . Table 1. Characteristics of hospitalized COVID-19 patients in the Sahloul UH from September 2020 to September 2022. Characteristics of Covid-19 hospitalized patients Variable N (%) Age (years) (mean/standard deviation) 55.65± 21.39 Length of stay (days) (Median [IQR]) 8 [4 – 13] Sex n (%) Male 1088 (55) Female 890 (45) Comorbidities 1350 (68.3) Hypertension 577 (42.7) Diabetes 387 (28.7) Heart disease 206 (15.3) Respiratory disease 83 (6.1) Renal disease 239 (17.7) Neurological disease 168 (12.4) Endocrine disease 143 (10.6) Obesity 53 (3.9) Other 176 (13) Confirmed COVID-19 cases were hospitalized in different departments of the hospital. The transplant unit covered the most important percentage of hospitalized patients (n=909; 32.72%), followed by the medical departments (n=903; 32.4%). The different percentages are detailed in Table 2 . Table 2. Distribution of COVID-19 hospitalized patients in Sahloul UH by department from September 2020 to September 2022. Department Numbers Percentages % Transplant unit 909 32.72 Reanimation departments 480 17.28 Surgery departments 486 17.49 Medical departments 903 32.51 Total 2778* 100 During the study period, 417 patients died due to COVID-19 (21.1%). The highest mortality rate was reported in the reanimation departments (60.7%) and the highe percent of discharges was reported in the medical departments (76.0%). The different outcomes are represented in the Table 3 . Table 3. Outcome of patients by discharge department in Sahloul University Hospital from September 2020 to September 2022. Departments Number of deaths 417 N (%) Number of deaths/number of patients per service (%) Number of discharges 1561 N (%) Number of discharges/number of patients per service (%) Reanimation departments (391) 253(60.7) 64.7 138(8.8) 35.3 Surgical departments (257) 21(5.0) 8.2 236(15.1) 91.8 Medical departments (1330) 143(34.3) 10.8 1187(76.0) 89.2 The deceased patients were males (60.4%) and had a mean age of 63.8 ±16.25 years. The median length of stay was 9 days. 5 – 16 The most frequent comorbidity was hypertension (55.7%). Trend of mortality rate The trend of death prevalence increased from 9.7% in November 2021 to 23.3% in June 2021. The curve drops to 15.6% in July 2021 to level off between 22.1% in August 2022 and 23.3% in January 2022 ( Figure 1 ). Figure 1. Trend curve of the death rate among hospitalized COVID-19 patients in Sahloul University Hospital of Sousse during the study period. Factors associated with mortality among COVID-19 patients in Sahloul University Hospital from September 2020 until September 2022 Kaplan–Meier curve of mortality During the study period, the overall survival of hospitalized COVID-19 patients in Sahloul University Hospital was 78.9%, and a total of 417 deaths were reported. The median survival was 30±3.11 days (95% CI [23.90-36.09]). The survival function is shown in Figure 2 . Figure 2. Kaplan–Meier survival curve of hospitalized COVID-19 patients in Sahloul University Hospital from September 2020 until September 2022. Survival univariate analysis of factors associated with mortality among COVID-19 patients in Sahloul University Hospital from September 2020 until September 2022 When examining the survival rates of several factors, it becomes evident that patients aged 65 years and above have a notably lower estimated survival rate, with a higher percentage of mortality (52.8% compared to 47.2%). Similarly, among the sexes, males exhibited a higher mortality rate (60.4% versus 39.6%). Other factors significantly affecting the survival curve were comorbidities, diabetes, hypertension, endocrine diseases, obesity, intubation, and ICU admission. These varied factors are summarized in Table 4 . Table 4. Factors associated with mortality among hospitalized COVID-19 patients in Sahloul University Hospital: survival univariate analysis. Variables HR [CI] p-value Gender 1.33[1.08;1.62] 0.05 Age 1.65 [1.36;2.00] <10 -3 Comorbidities 1.53[1.19;1.97] 0.001 Diabetes 1.31[1.05;1.63] 0.015 Hypertension 1.75[1.44;2.12] <10 -3 Cardiac disease 1.11[0.83;1.49] 0.477 Respiratory disease 1.22[0.79;1.88] 0.356 Renal disease 0.86[0.64;1.16] 0.332 Neurological disease 1.31[0.95;1.79] 0.098 Endocrine disease 1.52[1.12;2.05] 0.007 Obesity 2.42[1.66;3.51] <10 -3 Other medical history 0.72[0.51;1.02] 0.062 ICU admission 12.67[9.95;6.14] <10 -3 Oro tracheal intubation 4.55[3.74;5.54] <10 -3 Survival multilevel analysis among COVID-19 patients in Sahloul University Hospital from September 2020 until September 2022 The multilevel analysis showed the following independent factors associated with mortality: the presence of hypertension and ICU admission. Male gender is a Forced factor always found in the literature. More details are shown in Table 5 . Table 5. Multilevel survival analysis of hospitalized COVID-19 patients in Sahloul University Hospital between September 2020 and September 2022. Factors Adjusted HR CI95% p ICU 12.20 9.56-15.57 0.000 Hypertension 1.24 1.02-1.52 0.028 Gender 1.19 0.98-1.46 0.076 Discussion This study represents one of the first large case studies with confirmed COVID-19 cases at a regional level and provides the most relevant novelty about the region of Sousse. In the present work, data from hospitalized COVID-19-positive patients in a Tunisian university hospital were analyzed from the first case hospitalization. This study discusses independent predictive factors for hospital mortality of COVID-19 patients in a Tunisian tertiary care center since September 2020. Many studies were conducted in the same context, but the study period is different, as the majority analyzed data of some months or a maximum of one year, while our study included a longest period. 12 – 14 To identify the main predictive factors affecting the survival probability for COVID-19, survival analysis methods were used. Such analytic models can work with specific problems, with a data type that waits for the event to occur. In our study, the event mentioned was ‘death.’ We used Cox regression, which is the most appropriate method to analyze survival. Data extracted from methods with survival analysis are robust. 15 These data are helpful for making the right decision about each patient’s case according to the available treatment and medical instruments. Thus, this work will contribute at different levels, such as finding the survival probability for each patient, finding the effect of each feature on survival probability by calculating p value for each feature, and finally enhancing the COVID-19 system. 10 Many other works have studied clinical characteristics, outcomes, and predictive factors for mortality in hospitalized patients with COVID-19, 16 – 19 but few of them used survival analysis. Moreover, our study focuses on the positive effect of vaccination against death and length of stay. Thus, the insights gained from this study have the potential to enhance people’s reassurance, thereby addressing vaccine hesitancy and dispelling conspiracy beliefs. On the other hand, this study involved a large sample size that is needed to conflict with valid studies and representative results. One of the major strengths of our work was the exhaustivity of our data. Patients were enrolled and registered upon their hospitalization, and all their information was directly collected. This significantly minimized, or even eliminated, the bias of loss to follow-up in our study. From the first COVID-19 hospitalization in September 2020 until September 2022, 1978, hospitalizations due to COVID-19 were followed with a sex ratio of 1.2. The mean age and the median length of stay were 55.65± 21.39 years and 8 days, respectively. The most common comorbidity among the population was hypertension (42.7%). A similar study during 2020 in Sofia Bulgaria included 138 hospitalized COVID-19 patients. The mean age was 52.9 years, and males were the dominant sex (sex ratio: = 1.6). The mean age was significantly different compared to other studies. 20 Another study in early 2020 showed a higher mean age (65.5 years). 21 In other studies, the mean age was 70.5 (±15.7, 30–101) years. This difference can be related to the study population or the specific populations on which the research was conducted. Some studies have focused on a specific comorbidity in hospitalized COVID-19 patients, such as the characteristics of COVID-19 among hospitalized patients suffering from diabetes. 22 The current findings regarding the sex ratio align with emerging research that indicates a higher susceptibility for severe outcomes, including death, in men with COVID-19 compared to women. 23 Hypertension as the most frequent comorbidity was similar to the findings of many previous studies 24 In our study, the reported death rate varied from 9.7% in November 2020 to 21.1% in September 2022, which is close to the fatality rate found in a study in Mexico and the United States (12%). 25 , 26 However, this rate was lower than the reported death rate in an Italian hospital (33.3%). 27 This can be justified, as Italy has registered the most important mortality rates in the world, especially in the first period of the disease. It can also be justified due to differences in thresholds for hospitalization. A third possible explanation for variation in country/regional-specific case-fatality rates is the different strategies used for COVID-19 PCR testing. 28 Additionally, mortality rate differences can relate to the types of hospitals (public or private hospitals), and this can be explained by the difference in socioeconomic profiles. 29 In our study, the median survival time was 30 days (95% CI [23.9; 36.1]). At the end of the follow-up, 417 deaths were recorded. These findings came online with the results of a similar study. The median survival time was 12 days (95% CI 11.82–12.18). At the end of the follow-up, 21 408 deaths were recorded. 30 The overall survival rate in our study (78.9%) was higher than that in a study by Bonfada et al., which analyzed the survival of elderly patients hospitalized in the ICU (66.64%). 31 The median survival time was close to that reported in Brazil (19 days) and higher than that reported in Ethiopia (9 days), the USA (10 days) and Mexico (9 days). 30 , 32 , 33 When searching for the different predictive factors in the ‘Cox’ model, we found similar studies using the survival multilevel analysis conducted in Brazil, India, South India, Sweden and China. 30 , 33 – 38 Our study identified three statistically significant covariates sex, hypertension, and ICU admission - in our Cox proportional hazards model, all of which were separately identified in previous studies. Notably, gender’s significance in our model aligns with the study from Sweden, hypertension’s relevance matches findings from the study in Wuhan, and ICU admission’s impact mirrors conclusions drawn in the Brazilian study. These consistent results across different studies underscore the importance of these covariates in predicting the risk of death in our research context. Concerning sex-related factors, our analysis has confirmed a higher risk for male patients compared to females. The different prevalence of infection among patients according to sex was mentioned in other studies. 26 The reduced susceptibility of females to viral infections may be attributed to their inherent protection from factors associated with the X chromosome and sex hormones, both of which play crucial roles in both innate and adaptive immunity. 39 Notably, SARS-CoV-2, the virus responsible for COVID-19, utilizes ACE2 receptors located on pulmonary endothelial cells for entry, and it is worth noting that the ACE2 gene is situated on the X chromosome. 40 This genetic aspect could explain the higher prevalence of COVID-19 in men than in women. Additionally, in premenopausal women, the ovaries produce estradiol. However, after menopause, the ovaries significantly decrease estradiol production, equating with men. ICU admission emerged as a crucial factor strongly associated with 80.1% of deaths among confirmed cases of COVID-19. These results were in line with other studies that concluded that the ICU was a risk factor for disease severity, especially among men, as men admitted to the ICU were 5.42 and 1.39 times more likely to die, respectively, than those who did not enter these units. 41 Other covariates that demonstrated statistical significance in various separate studies were also found to be statistically significant in our univariate survival analysis. These covariates include age, 42 comorbidities , 43 diabetes, endocrine diseases, obesity, and the need for invasive ventilatory support . The present study confirmed that age above 65 years was significantly associated with death in patients with COVID-19 in univariate survival analysis (52.8% vs 47.2%; p<10-3). In a similar study using survival analysis, it was found that the hazard ratio increased with increasing age. For instance, in the group aged above 65 years, the patient is at 39.3 times higher risk of dying overall than a patient in a younger age group. 44 The significant comorbidities associated with mortality in our univariate analysis were hypertension, diabetes, endocrine diseases, and obesity. Similarly, comorbidities found in a Mexican study were hypertension, diabetes and obesity in COVID-19–positive patients. 25 Our findings can be explained by the high prevalence of diabetes, hypertension, and obesity in Tunisia. On the other hand, comorbidities are the main risk factor for hospitalization. Thus, the highest prevalence of these hospitalized patients already suffers from a comorbidity, and more importantly, the prevalence of hypertension in positive patients was higher than the population mean. 45 This observation could be elucidated by considering the detrimental impacts of comorbidities on the body’s autoimmune response and its capacity to manage metabolic stress, which are characteristic features of systemic diseases. Comorbidities, such as diabetes, hypertension, and cardiovascular diseases, often introduce a state of chronic inflammation and metabolic dysfunction within the body. These conditions can compromise the immune system’s ability to mount an effective defense against pathogenic agents, including viruses such as SARS-CoV-2. In essence, the presence of comorbidities not only weakens the body’s innate and adaptive immune responses but also creates an environment that is more conducive to the replication and spread of pathogens, potentially contributing to the increased susceptibility and severity of diseases such as COVID-19 in individuals with underlying health conditions. 46 Even studies that did not use survival analysis showed that older patients, men, and those with preexisting comorbidities such as hypertension, diabetes, cardiac diseases, renal diseases, respiratory diseases, neurological diseases and other health problems were the highly prevalent characteristics in our study. This comes in line with previous records in the literature, where these characteristics were associated with higher mortality rates and greater risk of negative health outcomes. 10 , 15 , 16 Conclusions Tunisia was one of the most affected countries with COVID-19 disease until vaccination became a potential resolution of the threat. Our study aimed to analyze the survival of patients admitted to a Tunisian tertiary care center (Sahloul University Hospital) to identify independent predictive factors for hospital COVID-19 mortality. This work is the first large case study with confirmed COVID-19 cases at a regional level and provides the most relevant novelty about the region of Sousse. The survival analysis made this work unique thanks to the robust results that it produces. This analysis showed three independent predictive factors associated with higher COVID-19 mortality: ICU hospitalization, hypertension, and sex. Ethics & consent The present Study was reviewed and approved by the ‘: ethics and research committee of Sahloul University Hospital and registered under the IRB approval number HS 58-2020 on 9 th July 2020. The committee is an academic body responsible for ensuring the ethical integrity of research conducted within the institution. This study was conducted with respect of the principles of the research ethics including patients’ anonymity and data confidentiality. Data was collected entirely from patient records, approval was obtained from the records holder to access and use the data. This ensures that all data used in the study complies with ethical standards and respects patient confidentiality and autonomy. The consent from participants was waived by the ethical approval committee. Author contributions The corresponding author attests that all listed authors meet the journals authorship criteria and that no others meeting the criteria have been omitted. Conceptualization, A. N, H.G; Methodology, H. G and A.N.; Software, H. G, A.N., and R.B.; Validation, H.S.L, and M.B.R; Formal Analysis, A.N.; Investigation A. N, H.G and R.B.; Data Curation A. N, R.B. and H.G. Writing—Original Draft Preparation, A.N.; Writing—Review and Editing, H. G, B. R, M.B.R and H.S.L.; Visualization B. S, H.B.H, B. K and A.B.C; Supervision M. K, H.G, W. N, H.T; Project Administration H. T, H.S.L, W. N and M.K. All authors approve the final manuscript submitted and agree to be personally accountable for the work. Data availability statement The prevention and safety of care department, Sahloul University Hospital-Sousse (Tunisia), as well as the corresponding author, have full access to all data. The data that support the findings of this study are not publicly available due to confidentiality concerns. The dataset includes exhaustive data of all hospitalized patients with COVID-19 at Sahloul University Hospital, as well as internal hospital information that cannot be shared in order to protect patient privacy and comply with institutional policies. Access to the data is restricted, and it is not available for sharing or publication. Anyone seeking access to the data should contact the corresponding author ( [email protected] ), who will then consult with the other authors to determine whether the data can be shared. The reader or reviewer who seeks for access to data should provide this information: Age, grade, institution, country, the reason to apply for access to the data, The data must be requested for legitimate research purposes, such as public health studies, academic research, or healthcare improvement projects. Applicants must submit a research proposal detailing the objectives, methods, and intended use of the data, Applicants must be affiliated with a recognized institution such as a university, research institute, or healthcare organization. Independent researchers may apply but must submit dditional documentation of their qualifications, the applicant must provide proof of ethical approval from their institution or a recognized ethics review board, and must confirm the Non-ommercial Use. Acknowledgments No acknowledgements. References 1. Huang YZ, Kuan CC: Vaccination to reduce severe COVID-19 and mortality in COVID-19 patients: a systematic review and meta-analysis. Eur. Rev. Med. Pharmacol. Sci. 2022; 26 (5): 1770–1776. PubMed Abstract | Publisher Full Text 2. World Health Organization: Tunisia: WHO Coronavirus Disease (COVID-19) Dashboard With Vaccination Data.2023 [cited 2023 Jan 30]. Reference Source 3. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 11 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Prevention and Security of Care, Sahloul University Hospital,, Sousse, Tunisia 2 LR20SP06, Sahloul University Hospital, Sousse, Tunisia 3 Faculty of Medicine of Sousse, University of Sousse, Sousse, Tunisia 4 Faculty of Medicine of Sousse, Department of Family Medicine, Research Laboratory LR12ES03, University of Sousse, Sousse, Tunisia 5 Department of Surgical Intensive Care, Sahloul University Hospital, Sousse, Tunisia 6 Department of Pharmacy, Faculty of Pharmacy of Monastir, University of Monastir, Tunisia, Sahloul University Hospital, Monastir, Tunisia 7 Laboratory of Microbiology, Sahloul University Hospital, Sousse, Tunisia ARWA NEFFATI Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation Hela Ghali Roles: Conceptualization, Data Curation, Investigation, Methodology, Software, Supervision, Writing – Review & Editing Rania Bannour Roles: Data Curation, Investigation, Software, Writing – Review & Editing Sana bhiri Roles: Visualization Habiba Ben hamada Roles: Visualization Balsam Kacem Roles: Visualization Mohamed Ben Rejeb Roles: Validation, Writing – Review & Editing Asma Ben Cheikh Roles: Visualization Mohamed Kahloul Roles: Project Administration, Supervision Walid Naija Roles: Project Administration, Supervision Halim Trabelsi Roles: Project Administration, Supervision Houyem Said Laatiri Roles: Project Administration, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 11 Oct 2024, 13:1215 https://doi.org/10.12688/f1000research.156234.1 Copyright © 2024 NEFFATI A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article NEFFATI A, Ghali H, Bannour R et al. Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :1215 ( https://doi.org/10.12688/f1000research.156234.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 11 Oct 2024 Views 0 Cite How to cite this report: Islam MM. Reviewer Report For: Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :1215 ( https://doi.org/10.5256/f1000research.171516.r353775 ) The direct URL for this report is: https://f1000research.com/articles/13-1215/v1#referee-response-353775 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 21 Jan 2025 Md Momin Islam , University of Dhaka, Dhaka, Dhaka Division, Bangladesh Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.171516.r353775 Thanks for giving me the opportunity. The overall presentation of the manuscript is appropriate, but some revisions are required. After incorporating the revisions, this manuscript could be accepted for possible publication in F1000Research. The author ... Continue reading READ ALL Thanks for giving me the opportunity. The overall presentation of the manuscript is appropriate, but some revisions are required. After incorporating the revisions, this manuscript could be accepted for possible publication in F1000Research. The author mentions that factors significantly affecting the survival curve include age, comorbidities, diabetes, hypertension, etc. It’s suggested that the author either only mention "comorbidities" or exclude specific conditions like diabetes and hypertension to avoid redundancy. The author should include current statistics on COVID-19 cases and fatalities, particularly the present situation in Tunisia. Please provide an update on the current state of COVID-19 in Tunisia. The introduction is too brief. The author should expand this section with more recent research and context. While the study objectives are clearly stated in the last paragraph, the introduction needs to better explain the motivation behind the study. Why was Cox regression chosen for this analysis? How does it compare to other regression models like Weibull, Log-logistic, or Log-normal regression? Please explain why Cox regression is the most suitable option. The total number of patients is listed as 2778, but the author mentions 1978 patients. Were any patients admitted to multiple departments simultaneously? Clarify this discrepancy. The author claims that patients aged 65 years and above have a significantly lower survival rate, with a higher mortality percentage (52.8% compared to 47.2%). However, age is presented as a continuous variable with a mean and standard deviation. If the age was categorized (65 or above vs. below 65), please provide a reference for this categorization. The discussion part is excellent, the author may consider comparing several risk factors with the study referenced below. https://doi.org/10.1007/s10900-020-00920-x In the discussion section, it could be better to include some findings that are dissimilar to your findings. Cross-check all references in the list and text, and also check reference styling. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Noor FM, Islam MM: Prevalence and Associated Risk Factors of Mortality Among COVID-19 Patients: A Meta-Analysis. J Community Health . 2020; 45 (6): 1270-1282 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Meta-analysis, Infectious disease, maternal and Child health, NCD I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Islam MM. Reviewer Report For: Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :1215 ( https://doi.org/10.5256/f1000research.171516.r353775 ) The direct URL for this report is: https://f1000research.com/articles/13-1215/v1#referee-response-353775 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 23 Jan 2025 ARWA NEFFATI , Department of Prevention and Security of Care, Sahloul University Hospital,, Sousse, Tunisia 23 Jan 2025 Author Response Subject: Response to Reviewer Comments – Manuscript [Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia] Dear Prof. Md Momin Islam , We ... Continue reading Subject: Response to Reviewer Comments – Manuscript [Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia] Dear Prof. Md Momin Islam , We sincerely thank you and the reviewers for the thoughtful and constructive comments on our manuscript titled " Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia." We have carefully addressed each point raised and made the necessary revisions to improve the manuscript. Please find below our detailed responses to your comments and a description of the changes made in the revised manuscript. Reviewer 's Comments: Comment 1: The author mentions that factors significantly affecting the survival curve include age, comorbidities, diabetes, hypertension, etc. It’s suggested that the author either only mention "comorbidities" or exclude specific conditions like diabetes and hypertension to avoid redundancy. Response: "We will clarify this point in the 'Results' section. We will delete the item ‘comorbidities’". Comment 2: The author should include current statistics on COVID-19 cases and fatalities, particularly the present situation in Tunisia. Please provide an update on the current state of COVID-19 in Tunisia Response: Thank you for this comment. As our study included the patients hospitalized between ‘September 2020 and September 2022’; we focused, in the ‘introduction part, on the situation of Covid-19 in Tunisia during that period(2020-2022) .” Tunisia is one of the most affected countries with a high rate of patients with acute respiratory syndrome coronavirus. In fact, 1145686 cases and 29249 deaths were reported as of September 2022” Comment 3: The introduction is too brief. The author should expand this section with more recent research and context. While the study objectives are clearly stated in the last paragraph, the introduction needs to better explain the motivation behind the study. Response: Yes of course. We will add more recent researches and more details in the introduction part. Comment 4: Why was Cox regression chosen for this analysis? How does it compare to other regression models like Weibull, Log-logistic, or Log-normal regression? Please explain why Cox regression is the most suitable option. Response: This is a very important question. We responded to this point in the ‘discussion part’ and put as reference an interesting article showing why ‘Cox regression is a robust method’. And here is another explanation: “Cox regression is often chosen for survival analysis because of its flexibility compared to other parametric regression models like Weibull, Log-logistic, or Log-normal regression. 1. Assumption of Proportional Hazards Cox regression assumes that the hazard ratios between groups are constant over time (the proportional hazards assumption). This assumption simplifies the model and focuses on the relative risk of the event occurring rather than estimating the baseline hazard function directly. 2. Semi-Parametric Nature Unlike Weibull, Log-logistic, or Log-normal models, which require specifying a distribution for the survival times, Cox regression does not make assumptions about the shape of the baseline hazard function. This makes Cox regression more robust and flexible for analyzing survival data with unknown or complex baseline hazard distributions. 3. Handling of Censored Data Cox regression effectively handles right-censored data (common in survival analysis) and provides unbiased estimates of the effect of covariates, even when not all events are observed. 5. Focus on Covariates The primary goal of Cox regression is to assess the effect of covariates on the hazard rate. This aligns well with studies aiming to understand relative risks or the influence of predictors, rather than estimating the exact survival probabilities. 6. Interpretation Cox regression provides hazard ratios, which are easily interpretable as the relative risk of an event occurring in one group compared to another. This simplicity in interpretation often makes it a preferred choice.” Comment 5: The total number of patients is listed as 2778, but the author mentions 1978 patients. Were any patients admitted to multiple departments simultaneously? Clarify this discrepancy. Response : Thank you for this valuable comment. Yes, we put a ‘*’ in front of the total number to clarify this point. We will add it in the manuscript under the table : ‘*: The difference existing between the population size and the total is due to the readmission of the patient in different departments which includes counting the same patient many times. ’ Comment 6: The author claims that patients aged 65 years and above have a significantly lower survival rate, with a higher mortality percentage (52.8% compared to 47.2%). However, age is presented as a continuous variable with a mean and standard deviation. If the age was categorized (65 or above vs. below 65), please provide a reference for this categorization. Response : We will clarify this point in the ‘methods’ section, and we will put a reference. Comment 7: The discussion part is excellent; the author may consider comparing several risk factors with the study referenced below. https://doi.org/10.1007/s10900-020-00920-x . Response : Thank you very much: We will consider this article and include it as part of our references. Comment 8: In the discussion section, it could be better to include some findings that are dissimilar to your findings. Response : Yes of course. We will add this part as soon as we are able to publish a modified version of our article. Comment 9: Cross-check all references in the list and text and check reference styling. Response : Thank you very much. We will check and modify the inappropriate references. Changes Made: We will incorporate all the changes based on the reviewers' comments into the revised version of our article, which will be updated as soon as it is published . We believe that these revisions have significantly improved the clarity and quality of our manuscript. We are grateful for the reviewers’ valuable insights and suggestions, which have helped us refine our work. Please do not hesitate to contact us if further clarification or additional revisions are required. Thank you for considering our revised manuscript. Sincerely, Arwa NEFFATI Subject: Response to Reviewer Comments – Manuscript [Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia] Dear Prof. Md Momin Islam , We sincerely thank you and the reviewers for the thoughtful and constructive comments on our manuscript titled " Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia." We have carefully addressed each point raised and made the necessary revisions to improve the manuscript. Please find below our detailed responses to your comments and a description of the changes made in the revised manuscript. Reviewer 's Comments: Comment 1: The author mentions that factors significantly affecting the survival curve include age, comorbidities, diabetes, hypertension, etc. It’s suggested that the author either only mention "comorbidities" or exclude specific conditions like diabetes and hypertension to avoid redundancy. Response: "We will clarify this point in the 'Results' section. We will delete the item ‘comorbidities’". Comment 2: The author should include current statistics on COVID-19 cases and fatalities, particularly the present situation in Tunisia. Please provide an update on the current state of COVID-19 in Tunisia Response: Thank you for this comment. As our study included the patients hospitalized between ‘September 2020 and September 2022’; we focused, in the ‘introduction part, on the situation of Covid-19 in Tunisia during that period(2020-2022) .” Tunisia is one of the most affected countries with a high rate of patients with acute respiratory syndrome coronavirus. In fact, 1145686 cases and 29249 deaths were reported as of September 2022” Comment 3: The introduction is too brief. The author should expand this section with more recent research and context. While the study objectives are clearly stated in the last paragraph, the introduction needs to better explain the motivation behind the study. Response: Yes of course. We will add more recent researches and more details in the introduction part. Comment 4: Why was Cox regression chosen for this analysis? How does it compare to other regression models like Weibull, Log-logistic, or Log-normal regression? Please explain why Cox regression is the most suitable option. Response: This is a very important question. We responded to this point in the ‘discussion part’ and put as reference an interesting article showing why ‘Cox regression is a robust method’. And here is another explanation: “Cox regression is often chosen for survival analysis because of its flexibility compared to other parametric regression models like Weibull, Log-logistic, or Log-normal regression. 1. Assumption of Proportional Hazards Cox regression assumes that the hazard ratios between groups are constant over time (the proportional hazards assumption). This assumption simplifies the model and focuses on the relative risk of the event occurring rather than estimating the baseline hazard function directly. 2. Semi-Parametric Nature Unlike Weibull, Log-logistic, or Log-normal models, which require specifying a distribution for the survival times, Cox regression does not make assumptions about the shape of the baseline hazard function. This makes Cox regression more robust and flexible for analyzing survival data with unknown or complex baseline hazard distributions. 3. Handling of Censored Data Cox regression effectively handles right-censored data (common in survival analysis) and provides unbiased estimates of the effect of covariates, even when not all events are observed. 5. Focus on Covariates The primary goal of Cox regression is to assess the effect of covariates on the hazard rate. This aligns well with studies aiming to understand relative risks or the influence of predictors, rather than estimating the exact survival probabilities. 6. Interpretation Cox regression provides hazard ratios, which are easily interpretable as the relative risk of an event occurring in one group compared to another. This simplicity in interpretation often makes it a preferred choice.” Comment 5: The total number of patients is listed as 2778, but the author mentions 1978 patients. Were any patients admitted to multiple departments simultaneously? Clarify this discrepancy. Response : Thank you for this valuable comment. Yes, we put a ‘*’ in front of the total number to clarify this point. We will add it in the manuscript under the table : ‘*: The difference existing between the population size and the total is due to the readmission of the patient in different departments which includes counting the same patient many times. ’ Comment 6: The author claims that patients aged 65 years and above have a significantly lower survival rate, with a higher mortality percentage (52.8% compared to 47.2%). However, age is presented as a continuous variable with a mean and standard deviation. If the age was categorized (65 or above vs. below 65), please provide a reference for this categorization. Response : We will clarify this point in the ‘methods’ section, and we will put a reference. Comment 7: The discussion part is excellent; the author may consider comparing several risk factors with the study referenced below. https://doi.org/10.1007/s10900-020-00920-x . Response : Thank you very much: We will consider this article and include it as part of our references. Comment 8: In the discussion section, it could be better to include some findings that are dissimilar to your findings. Response : Yes of course. We will add this part as soon as we are able to publish a modified version of our article. Comment 9: Cross-check all references in the list and text and check reference styling. Response : Thank you very much. We will check and modify the inappropriate references. Changes Made: We will incorporate all the changes based on the reviewers' comments into the revised version of our article, which will be updated as soon as it is published . We believe that these revisions have significantly improved the clarity and quality of our manuscript. We are grateful for the reviewers’ valuable insights and suggestions, which have helped us refine our work. Please do not hesitate to contact us if further clarification or additional revisions are required. Thank you for considering our revised manuscript. Sincerely, Arwa NEFFATI Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 23 Jan 2025 ARWA NEFFATI , Department of Prevention and Security of Care, Sahloul University Hospital,, Sousse, Tunisia 23 Jan 2025 Author Response Subject: Response to Reviewer Comments – Manuscript [Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia] Dear Prof. Md Momin Islam , We ... Continue reading Subject: Response to Reviewer Comments – Manuscript [Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia] Dear Prof. Md Momin Islam , We sincerely thank you and the reviewers for the thoughtful and constructive comments on our manuscript titled " Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia." We have carefully addressed each point raised and made the necessary revisions to improve the manuscript. Please find below our detailed responses to your comments and a description of the changes made in the revised manuscript. Reviewer 's Comments: Comment 1: The author mentions that factors significantly affecting the survival curve include age, comorbidities, diabetes, hypertension, etc. It’s suggested that the author either only mention "comorbidities" or exclude specific conditions like diabetes and hypertension to avoid redundancy. Response: "We will clarify this point in the 'Results' section. We will delete the item ‘comorbidities’". Comment 2: The author should include current statistics on COVID-19 cases and fatalities, particularly the present situation in Tunisia. Please provide an update on the current state of COVID-19 in Tunisia Response: Thank you for this comment. As our study included the patients hospitalized between ‘September 2020 and September 2022’; we focused, in the ‘introduction part, on the situation of Covid-19 in Tunisia during that period(2020-2022) .” Tunisia is one of the most affected countries with a high rate of patients with acute respiratory syndrome coronavirus. In fact, 1145686 cases and 29249 deaths were reported as of September 2022” Comment 3: The introduction is too brief. The author should expand this section with more recent research and context. While the study objectives are clearly stated in the last paragraph, the introduction needs to better explain the motivation behind the study. Response: Yes of course. We will add more recent researches and more details in the introduction part. Comment 4: Why was Cox regression chosen for this analysis? How does it compare to other regression models like Weibull, Log-logistic, or Log-normal regression? Please explain why Cox regression is the most suitable option. Response: This is a very important question. We responded to this point in the ‘discussion part’ and put as reference an interesting article showing why ‘Cox regression is a robust method’. And here is another explanation: “Cox regression is often chosen for survival analysis because of its flexibility compared to other parametric regression models like Weibull, Log-logistic, or Log-normal regression. 1. Assumption of Proportional Hazards Cox regression assumes that the hazard ratios between groups are constant over time (the proportional hazards assumption). This assumption simplifies the model and focuses on the relative risk of the event occurring rather than estimating the baseline hazard function directly. 2. Semi-Parametric Nature Unlike Weibull, Log-logistic, or Log-normal models, which require specifying a distribution for the survival times, Cox regression does not make assumptions about the shape of the baseline hazard function. This makes Cox regression more robust and flexible for analyzing survival data with unknown or complex baseline hazard distributions. 3. Handling of Censored Data Cox regression effectively handles right-censored data (common in survival analysis) and provides unbiased estimates of the effect of covariates, even when not all events are observed. 5. Focus on Covariates The primary goal of Cox regression is to assess the effect of covariates on the hazard rate. This aligns well with studies aiming to understand relative risks or the influence of predictors, rather than estimating the exact survival probabilities. 6. Interpretation Cox regression provides hazard ratios, which are easily interpretable as the relative risk of an event occurring in one group compared to another. This simplicity in interpretation often makes it a preferred choice.” Comment 5: The total number of patients is listed as 2778, but the author mentions 1978 patients. Were any patients admitted to multiple departments simultaneously? Clarify this discrepancy. Response : Thank you for this valuable comment. Yes, we put a ‘*’ in front of the total number to clarify this point. We will add it in the manuscript under the table : ‘*: The difference existing between the population size and the total is due to the readmission of the patient in different departments which includes counting the same patient many times. ’ Comment 6: The author claims that patients aged 65 years and above have a significantly lower survival rate, with a higher mortality percentage (52.8% compared to 47.2%). However, age is presented as a continuous variable with a mean and standard deviation. If the age was categorized (65 or above vs. below 65), please provide a reference for this categorization. Response : We will clarify this point in the ‘methods’ section, and we will put a reference. Comment 7: The discussion part is excellent; the author may consider comparing several risk factors with the study referenced below. https://doi.org/10.1007/s10900-020-00920-x . Response : Thank you very much: We will consider this article and include it as part of our references. Comment 8: In the discussion section, it could be better to include some findings that are dissimilar to your findings. Response : Yes of course. We will add this part as soon as we are able to publish a modified version of our article. Comment 9: Cross-check all references in the list and text and check reference styling. Response : Thank you very much. We will check and modify the inappropriate references. Changes Made: We will incorporate all the changes based on the reviewers' comments into the revised version of our article, which will be updated as soon as it is published . We believe that these revisions have significantly improved the clarity and quality of our manuscript. We are grateful for the reviewers’ valuable insights and suggestions, which have helped us refine our work. Please do not hesitate to contact us if further clarification or additional revisions are required. Thank you for considering our revised manuscript. Sincerely, Arwa NEFFATI Subject: Response to Reviewer Comments – Manuscript [Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia] Dear Prof. Md Momin Islam , We sincerely thank you and the reviewers for the thoughtful and constructive comments on our manuscript titled " Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia." We have carefully addressed each point raised and made the necessary revisions to improve the manuscript. Please find below our detailed responses to your comments and a description of the changes made in the revised manuscript. Reviewer 's Comments: Comment 1: The author mentions that factors significantly affecting the survival curve include age, comorbidities, diabetes, hypertension, etc. It’s suggested that the author either only mention "comorbidities" or exclude specific conditions like diabetes and hypertension to avoid redundancy. Response: "We will clarify this point in the 'Results' section. We will delete the item ‘comorbidities’". Comment 2: The author should include current statistics on COVID-19 cases and fatalities, particularly the present situation in Tunisia. Please provide an update on the current state of COVID-19 in Tunisia Response: Thank you for this comment. As our study included the patients hospitalized between ‘September 2020 and September 2022’; we focused, in the ‘introduction part, on the situation of Covid-19 in Tunisia during that period(2020-2022) .” Tunisia is one of the most affected countries with a high rate of patients with acute respiratory syndrome coronavirus. In fact, 1145686 cases and 29249 deaths were reported as of September 2022” Comment 3: The introduction is too brief. The author should expand this section with more recent research and context. While the study objectives are clearly stated in the last paragraph, the introduction needs to better explain the motivation behind the study. Response: Yes of course. We will add more recent researches and more details in the introduction part. Comment 4: Why was Cox regression chosen for this analysis? How does it compare to other regression models like Weibull, Log-logistic, or Log-normal regression? Please explain why Cox regression is the most suitable option. Response: This is a very important question. We responded to this point in the ‘discussion part’ and put as reference an interesting article showing why ‘Cox regression is a robust method’. And here is another explanation: “Cox regression is often chosen for survival analysis because of its flexibility compared to other parametric regression models like Weibull, Log-logistic, or Log-normal regression. 1. Assumption of Proportional Hazards Cox regression assumes that the hazard ratios between groups are constant over time (the proportional hazards assumption). This assumption simplifies the model and focuses on the relative risk of the event occurring rather than estimating the baseline hazard function directly. 2. Semi-Parametric Nature Unlike Weibull, Log-logistic, or Log-normal models, which require specifying a distribution for the survival times, Cox regression does not make assumptions about the shape of the baseline hazard function. This makes Cox regression more robust and flexible for analyzing survival data with unknown or complex baseline hazard distributions. 3. Handling of Censored Data Cox regression effectively handles right-censored data (common in survival analysis) and provides unbiased estimates of the effect of covariates, even when not all events are observed. 5. Focus on Covariates The primary goal of Cox regression is to assess the effect of covariates on the hazard rate. This aligns well with studies aiming to understand relative risks or the influence of predictors, rather than estimating the exact survival probabilities. 6. Interpretation Cox regression provides hazard ratios, which are easily interpretable as the relative risk of an event occurring in one group compared to another. This simplicity in interpretation often makes it a preferred choice.” Comment 5: The total number of patients is listed as 2778, but the author mentions 1978 patients. Were any patients admitted to multiple departments simultaneously? Clarify this discrepancy. Response : Thank you for this valuable comment. Yes, we put a ‘*’ in front of the total number to clarify this point. We will add it in the manuscript under the table : ‘*: The difference existing between the population size and the total is due to the readmission of the patient in different departments which includes counting the same patient many times. ’ Comment 6: The author claims that patients aged 65 years and above have a significantly lower survival rate, with a higher mortality percentage (52.8% compared to 47.2%). However, age is presented as a continuous variable with a mean and standard deviation. If the age was categorized (65 or above vs. below 65), please provide a reference for this categorization. Response : We will clarify this point in the ‘methods’ section, and we will put a reference. Comment 7: The discussion part is excellent; the author may consider comparing several risk factors with the study referenced below. https://doi.org/10.1007/s10900-020-00920-x . Response : Thank you very much: We will consider this article and include it as part of our references. Comment 8: In the discussion section, it could be better to include some findings that are dissimilar to your findings. Response : Yes of course. We will add this part as soon as we are able to publish a modified version of our article. Comment 9: Cross-check all references in the list and text and check reference styling. Response : Thank you very much. We will check and modify the inappropriate references. Changes Made: We will incorporate all the changes based on the reviewers' comments into the revised version of our article, which will be updated as soon as it is published . We believe that these revisions have significantly improved the clarity and quality of our manuscript. We are grateful for the reviewers’ valuable insights and suggestions, which have helped us refine our work. Please do not hesitate to contact us if further clarification or additional revisions are required. Thank you for considering our revised manuscript. Sincerely, Arwa NEFFATI Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 11 Oct 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 11 Oct 24 read Md Momin Islam , University of Dhaka, Dhaka, Bangladesh Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Islam M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 21 Jan 2025 | for Version 1 Md Momin Islam , University of Dhaka, Dhaka, Dhaka Division, Bangladesh 0 Views copyright © 2025 Islam M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thanks for giving me the opportunity. The overall presentation of the manuscript is appropriate, but some revisions are required. After incorporating the revisions, this manuscript could be accepted for possible publication in F1000Research. The author mentions that factors significantly affecting the survival curve include age, comorbidities, diabetes, hypertension, etc. It’s suggested that the author either only mention "comorbidities" or exclude specific conditions like diabetes and hypertension to avoid redundancy. The author should include current statistics on COVID-19 cases and fatalities, particularly the present situation in Tunisia. Please provide an update on the current state of COVID-19 in Tunisia. The introduction is too brief. The author should expand this section with more recent research and context. While the study objectives are clearly stated in the last paragraph, the introduction needs to better explain the motivation behind the study. Why was Cox regression chosen for this analysis? How does it compare to other regression models like Weibull, Log-logistic, or Log-normal regression? Please explain why Cox regression is the most suitable option. The total number of patients is listed as 2778, but the author mentions 1978 patients. Were any patients admitted to multiple departments simultaneously? Clarify this discrepancy. The author claims that patients aged 65 years and above have a significantly lower survival rate, with a higher mortality percentage (52.8% compared to 47.2%). However, age is presented as a continuous variable with a mean and standard deviation. If the age was categorized (65 or above vs. below 65), please provide a reference for this categorization. The discussion part is excellent, the author may consider comparing several risk factors with the study referenced below. https://doi.org/10.1007/s10900-020-00920-x In the discussion section, it could be better to include some findings that are dissimilar to your findings. Cross-check all references in the list and text, and also check reference styling. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Noor FM, Islam MM: Prevalence and Associated Risk Factors of Mortality Among COVID-19 Patients: A Meta-Analysis. J Community Health . 2020; 45 (6): 1270-1282 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Meta-analysis, Infectious disease, maternal and Child health, NCD I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 23 Jan 2025 ARWA NEFFATI, Department of Prevention and Security of Care, Sahloul University Hospital,, Sousse, Tunisia Subject: Response to Reviewer Comments – Manuscript [Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia] Dear Prof. Md Momin Islam , We sincerely thank you and the reviewers for the thoughtful and constructive comments on our manuscript titled " Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia." We have carefully addressed each point raised and made the necessary revisions to improve the manuscript. Please find below our detailed responses to your comments and a description of the changes made in the revised manuscript. Reviewer 's Comments: Comment 1: The author mentions that factors significantly affecting the survival curve include age, comorbidities, diabetes, hypertension, etc. It’s suggested that the author either only mention "comorbidities" or exclude specific conditions like diabetes and hypertension to avoid redundancy. Response: "We will clarify this point in the 'Results' section. We will delete the item ‘comorbidities’". Comment 2: The author should include current statistics on COVID-19 cases and fatalities, particularly the present situation in Tunisia. Please provide an update on the current state of COVID-19 in Tunisia Response: Thank you for this comment. As our study included the patients hospitalized between ‘September 2020 and September 2022’; we focused, in the ‘introduction part, on the situation of Covid-19 in Tunisia during that period(2020-2022) .” Tunisia is one of the most affected countries with a high rate of patients with acute respiratory syndrome coronavirus. In fact, 1145686 cases and 29249 deaths were reported as of September 2022” Comment 3: The introduction is too brief. The author should expand this section with more recent research and context. While the study objectives are clearly stated in the last paragraph, the introduction needs to better explain the motivation behind the study. Response: Yes of course. We will add more recent researches and more details in the introduction part. Comment 4: Why was Cox regression chosen for this analysis? How does it compare to other regression models like Weibull, Log-logistic, or Log-normal regression? Please explain why Cox regression is the most suitable option. Response: This is a very important question. We responded to this point in the ‘discussion part’ and put as reference an interesting article showing why ‘Cox regression is a robust method’. And here is another explanation: “Cox regression is often chosen for survival analysis because of its flexibility compared to other parametric regression models like Weibull, Log-logistic, or Log-normal regression. 1. Assumption of Proportional Hazards Cox regression assumes that the hazard ratios between groups are constant over time (the proportional hazards assumption). This assumption simplifies the model and focuses on the relative risk of the event occurring rather than estimating the baseline hazard function directly. 2. Semi-Parametric Nature Unlike Weibull, Log-logistic, or Log-normal models, which require specifying a distribution for the survival times, Cox regression does not make assumptions about the shape of the baseline hazard function. This makes Cox regression more robust and flexible for analyzing survival data with unknown or complex baseline hazard distributions. 3. Handling of Censored Data Cox regression effectively handles right-censored data (common in survival analysis) and provides unbiased estimates of the effect of covariates, even when not all events are observed. 5. Focus on Covariates The primary goal of Cox regression is to assess the effect of covariates on the hazard rate. This aligns well with studies aiming to understand relative risks or the influence of predictors, rather than estimating the exact survival probabilities. 6. Interpretation Cox regression provides hazard ratios, which are easily interpretable as the relative risk of an event occurring in one group compared to another. This simplicity in interpretation often makes it a preferred choice.” Comment 5: The total number of patients is listed as 2778, but the author mentions 1978 patients. Were any patients admitted to multiple departments simultaneously? Clarify this discrepancy. Response : Thank you for this valuable comment. Yes, we put a ‘*’ in front of the total number to clarify this point. We will add it in the manuscript under the table : ‘*: The difference existing between the population size and the total is due to the readmission of the patient in different departments which includes counting the same patient many times. ’ Comment 6: The author claims that patients aged 65 years and above have a significantly lower survival rate, with a higher mortality percentage (52.8% compared to 47.2%). However, age is presented as a continuous variable with a mean and standard deviation. If the age was categorized (65 or above vs. below 65), please provide a reference for this categorization. Response : We will clarify this point in the ‘methods’ section, and we will put a reference. Comment 7: The discussion part is excellent; the author may consider comparing several risk factors with the study referenced below. https://doi.org/10.1007/s10900-020-00920-x . Response : Thank you very much: We will consider this article and include it as part of our references. Comment 8: In the discussion section, it could be better to include some findings that are dissimilar to your findings. Response : Yes of course. We will add this part as soon as we are able to publish a modified version of our article. Comment 9: Cross-check all references in the list and text and check reference styling. Response : Thank you very much. We will check and modify the inappropriate references. Changes Made: We will incorporate all the changes based on the reviewers' comments into the revised version of our article, which will be updated as soon as it is published . We believe that these revisions have significantly improved the clarity and quality of our manuscript. We are grateful for the reviewers’ valuable insights and suggestions, which have helped us refine our work. Please do not hesitate to contact us if further clarification or additional revisions are required. Thank you for considering our revised manuscript. Sincerely, Arwa NEFFATI View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Islam MM. Peer Review Report For: Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia [version 1; peer review: 1 approved with reservations] . F1000Research 2024, 13 :1215 ( https://doi.org/10.5256/f1000research.171516.r353775) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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