The novel coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), was first identified in December 2019 in Wuhan, China, and rapidly evolved into a global pandemic (1). Initially linked to zoonotic transmission from a seafood market in Wuhan, human-to-human transmission soon became evident, significantly accelerating its global spread (2). As of late 2024, the global impact of the COVID-19 pandemic has been substantial. According to the World Health Organization (WHO), over 776.8 million confirmed cases and more than 7 million confirmed deaths have been reported worldwide since the onset of the pandemic. However, the actual death toll is likely higher, with estimates suggesting that the true number of COVID-19-related deaths could exceed 20 million (3).

In Palestine, the first COVID-19 cases were reported in early March 2020. By late 2021, Palestine had confirmed over 465,000 COVID-19 cases, with approximately 4,900 fatalities, and more than 3,000 patients admitted to intensive care units (ICUs) in governmental hospitals (52). The rapid increase in critically ill COVID-19 patients presented significant challenges to the Palestinian healthcare system, highlighting its vulnerability due to limited ICU capacity and medical resources.

Identifying risk factors for ICU admission among COVID-19 patients is essential for developing targeted strategies to reduce morbidity and manage healthcare resources effectively. Prior international research indicated that various clinical and demographic factors significantly influenced the severity of COVID-19 infections. Key factors frequently associated with increased ICU admission risk include advanced age, male gender, obesity, smoking, diabetes, hypertension, cardiovascular and respiratory comorbidities, and specific laboratory abnormalities such as elevated inflammatory markers and abnormal coagulation profiles (2, 4, 5).

Numerous large-scale studies and meta-analyses have identified consistent predictors of ICU admission in COVID-19 patients. These include advanced age, male sex, obesity, cardiovascular disease, diabetes, elevated inflammatory markers (e.g., CRP, ferritin, LDH), lymphopenia, and abnormal coagulation parameters (6–8). A Prospective cohort study conducted by Cidade et al. (9) found that elevated triglycerides, procalcitonin, and CRP were among the top predictors of ICU admission. Similarly, other studies emphasized the prognostic value of D-dimer, neutrophilia, and LDH as strong laboratory indicators of disease severity (10).

Furthermore, clinical indicators such as hypoxia, elevated respiratory rate, and early need for oxygen supplementation have been consistently associated with ICU admission risk (11). These parameters inform triage algorithms in many healthcare systems. However, it is important to note that most of the available data derive from high-income countries with well-resourced healthcare infrastructures.

Recent studies from the MENA region have explored ICU predictors in COVID-19 patients. In Jordan, Mayyas et al. (12) found that ICU stay and mortality were associated with age, neutrophil-to-lymphocyte ratio, CRP, creatinine, and ventilation. An Egyptian study reported correlations with age, obesity, random blood sugar, inflammatory markers, and renal/liver dysfunction (13). Similarly, in Saudi Arabia, ICU admission was linked to age, diabetes, heart failure, CRP, and respiratory distress, while mortality was strongly predicted by oxygen desaturation, thrombocytopenia, and elevated troponin (14). While these studies offer valuable insights, most are limited by single-center design and lack multivariable adjustment. Palestine remains underrepresented, highlighting the need for context-specific data.

Furthermore, most existing ICU risk prediction models have been developed in high-income settings, like Palestine, with robust health infrastructure, limiting their applicability to health systems constrained by conflict, occupation, or chronic shortages in ICU capacity and diagnostics. Palestine, as a lower-middle-income setting under prolonged political and logistical strain, presents a unique case to explore how standard predictors perform in resource-limited environments. Insights from such settings are critical to closing global evidence gaps and informing more inclusive pandemic preparedness strategies worldwide.

Therefore, the aim of this study is to explicitly identify and evaluate the risk factors associated with ICU admission among COVID-19 patients hospitalized in governmental hospitals across the West Bank, Palestine. Specifically, the study seeks to assess demographic characteristics, clinical presentations, comorbidities, laboratory, and radiological findings, and treatment regimens as potential predictors for ICU admission. These findings are anticipated to inform clinical practice guidelines, support health policy decisions, and enhance preparedness to mitigate the impact of COVID-19 in Palestine.

The coronavirus disease 2019 (COVID-19), caused by the novel SARS-CoV-2 virus, has strained healthcare systems globally, with particularly severe consequences in resource-limited and conflict-affected regions. Identifying patients at high risk for severe outcomes, such as intensive care unit (ICU) admission, is essential for optimizing resource allocation and guiding clinical decision-making (2, 5). While global studies have established several risk factors—including advanced age, obesity, comorbid conditions, and elevated inflammatory markers—there is a significant gap in context-specific data from low-income or politically unstable regions such as Palestine (15, 16). This study aims to fill that gap by investigating the predictors of ICU admission among hospitalized COVID-19 patients across multiple governmental hospitals in the West Bank, thereby providing localized insights to strengthen clinical triage strategies and public health policy.

In the current study, the nature of the patient’s occupation was found to be significantly associated with ICU admission among COVID-19 patients, a finding consistent with that of (17). Specifically, ICU admission was more common among unemployed individuals. This may be attributed to the economic impact of the pandemic, during which many people lost their jobs due to widespread lockdowns and the closure of businesses, factories, and public events. During this period, quarantine measures were enforced, and social activities such as weddings and family gatherings were halted. Only healthcare workers maintained consistent exposure due to their essential roles. While our study showed higher ICU admissions among the unemployed, prior research has reported greater ICU admissions among healthcare professionals (18). This discrepancy may be explained by the increased risk of infection faced by frontline workers due to repeated exposure, work in high-risk environments, and frequent interaction with infected patients. Moreover, the inherently hazardous nature of medical work, especially in the context of airborne and respiratory diseases like COVID-19, further elevates the risk of severe outcomes among this group.

In this study, the hospital of admission was identified as a significant predictor of ICU admission and mortality among COVID-19 patients. Specifically, Dura Hospital recorded the highest percentage of ICU admissions and deaths, whereas Red Crescent Hospital had the lowest. This disparity may be explained by several factors. Dura Hospital, located in the southern region, served a broader catchment area and admitted a higher volume of critically ill patients during the study period. In contrast, Red Crescent Hospital was newly established, had limited ICU capacity, admitted predominantly non-critical cases, and operated with a smaller and less experienced medical staff. These contextual differences in infrastructure, case severity, and clinical capacity likely contributed to the observed variations in outcomes across hospitals (19).

The results of the current study demonstrated a statistically significant association between blood group and ICU admission among COVID-19 patients. This finding aligns with the results of (20), who also reported higher ICU admission rates among patients with blood group A. A possible explanation is that individuals with blood group O, considered universal donors, tend to exhibit more favorable immune responses and overall physiological resilience compared to those with blood group A. Blood groups are determined by the presence of specific antigens on the surface of red blood cells (RBCs), which may influence susceptibility to certain infections, including SARS-CoV-2. The A blood group, in particular, may be associated with increased vulnerability to severe outcomes, including ICU admission. However, it is important to note that some studies have not found a statistically significant relationship between blood group and the risk of ICU admission, such as the findings reported by (21).

In this study, obesity was found to be significantly associated with ICU admission among COVID-19 patients, aligning with findings reported by Kim et al. (18). Obesity is already recognized as a major risk factor for severe COVID-19 outcomes due to its frequent association with comorbid conditions such as cardiovascular disease, diabetes, and hypertension. Furthermore, obese individuals often face physiological complications that can exacerbate disease severity, including obstructive sleep apnea—which may increase pulmonary hypertension—and a higher body mass index (BMI), which can complicate clinical management in hospital settings. These challenges may be further compounded by chronic low-grade inflammation and immune dysregulation commonly seen in obese patients. However, it is worth noting that not all studies have reported a statistically significant relationship between obesity and ICU admission; for example, Plataki et al. (22) did not find such an association in their analysis (22).

Our findings revealed a statistically significant relationship between smoking status and ICU admission among COVID-19 patients, corroborating the study by Kim et al. (18). Smoking is a well-established risk factor that contributes to increased severity of pulmonary infections through mechanisms such as airway damage, lung inflammation, and diminished respiratory and immune function. Moreover, smoking is linked to a range of comorbidities, including cardiovascular disease, chronic obstructive pulmonary disease (COPD), lung cancer, and diabetes, all of which may compound the risk of critical illness in COVID-19 patients (23). Smoking has also been associated with more severe influenza infections requiring hospitalization, which may similarly affect COVID-19 outcomes. One proposed mechanism involves smoking-induced overexpression of angiotensin-converting enzyme 2 (ACE2) receptors in the lungs, which are known to facilitate SARS-CoV-2 cellular entry and viral propagation, thereby increasing the likelihood of ICU admission and mortality (18).

Kim et al. (18) reported a statistically significant association between pregnancy and ICU admission among COVID-19 patients, which aligns with the findings of the current study. Pregnancy induces various physiological and immunological changes that may increase susceptibility to infectious diseases, including COVID-19. Pregnant women may experience more severe manifestations of respiratory illnesses due to altered immune responses and cardiopulmonary adaptations. Additionally, pregnancy-related complications such as gestational diabetes and hypertension may further exacerbate the severity of COVID-19, increasing the risk of ICU admission and potentially mortality. These overlapping risk factors underscore the need for heightened surveillance and targeted care strategies for pregnant women infected with SARS-CoV-2.

Some studies have also discussed that pregnant women experience a significant increase in blood volume, particularly plasma, by up to 50% around the 34th week of gestation. This physiological change ensures adequate perfusion of the uterus, fetus, and placenta. However, the resultant fluid overload can lead to edema—a condition that may compromise respiratory function and increase vulnerability to infections, including COVID-19. Such changes may heighten the likelihood of ICU admission among pregnant women (18). Moreover, according to Lokken et al. (24), pregnant individuals with COVID-19 are at higher risk for severe outcomes, including hospitalization and intensive care, particularly in the third trimester, which coincides with peak hemodynamic changes (24).

Although numerous previous studies have reported a statistically significant association between advanced age and ICU admission among COVID-19 patients (2, 24–26), the current study did not observe a significant relationship between age and ICU admission. This may be explained by the relatively homogenous age distribution in our sample, with most patients falling within the middle-age range (35–65 years), limiting the statistical contrast between younger and older groups. Additionally, ICU admission criteria were based primarily on clinical deterioration rather than chronological age, as outlined in the Ministry of Health protocols, which may have attenuated age-related effects observed in other settings.

In this study, the majority of participants were female (n = 120, 60%). However, gender was not found to be statistically associated with ICU admission among COVID-19 patients. This finding contrasts with the results of Kaeuffer et al. (25) and Noor and Islam (26), who reported male gender as a significant risk factor for severe COVID-19 outcomes and ICU admission. The absence of a significant association in our study may be due to the relatively balanced distribution of male and female participants across ICU and non-ICU groups, which could diminish any observable differences. Consequently, gender alone may not serve as a reliable predictor of ICU admission in this particular cohort of hospitalized COVID-19 patients.

The results showed no statistically significant association between educational level and ICU admission among COVID-19 patients (p = 0.112). In this study, 69 participants had higher education, 8 were illiterate, 63 had primary education, and 60 held a diploma or bachelor’s degree. Although participants with higher education represented a notable proportion (34.5%), this did not translate into significant differences in ICU admission rates. This finding aligns with broader literature suggesting that while education is often associated with general health outcomes, its direct effect on ICU admission during COVID-19 may be mediated by other factors such as comorbidities and healthcare access (27–29).

This study found no statistically significant association between social status and ICU admission among COVID-19 patients. However, previous research has shown that the quality of life during periods of widespread social restrictions can be influenced by factors such as education level, employment status, and financial stability. In particular, the pandemic exacerbated existing burdens on married women due to their multifaceted roles as caregivers, homemakers, and often the primary managers of household responsibilities. With the imposition of lockdowns and the closure of schools and public facilities, married women were disproportionately affected by increased domestic workload and childcare demands. These added responsibilities have been linked to deteriorating mental health, manifesting as heightened stress, anxiety, depression, and emotional fatigue (30).

The results of this study showed no statistically significant association between place of residence and ICU admission among Palestinian COVID-19 patients. Similarly, monthly income did not demonstrate a significant relationship with ICU admission. This may be attributed to the fact that all patients were admitted to governmental hospitals, where the treatment protocol is standardized and uniform regardless of socioeconomic status. Consequently, disparities in healthcare access and treatment quality based on income were minimized. However, it is worth noting that patients from higher-income brackets may have had better access to certain medications not readily available in government hospitals, such as Tocilizumab (Actemra), which could influence outcomes in subtle ways. Recent literature supports the notion that socioeconomic inequalities may influence outcomes in broader healthcare settings, but such effects may be attenuated in standardized public health systems (31).

This study found no significant relationship between patients’ medical history (i.e., comorbidities) and ICU admission or death among COVID-19 patients in the West Bank, Palestine. This contrasts with findings from other international studies, such as those conducted in France, the United States, and Bangladesh, which reported a strong link between comorbidities and severe COVID-19 outcomes (18, 25, 26). One possible explanation for this discrepancy is the age distribution of our sample. The majority of ICU-admitted patients in this cohort were relatively young, ranging from 35 to 45 years old. The uniformity of age distribution across groups may have minimized the expected effect of comorbidities. Additionally, critical illness in these patients may have been driven more by aggressive immune responses, such as cytokine storm syndrome, rather than underlying chronic diseases (32). It is also notable that other laboratory markers—such as elevated CRP, ferritin levels >900 μg/L, D-dimer >1,500 μg/L, body temperature >39°C, respiratory rate >30, and oxygen saturation <88%—were frequently abnormal but could not be included in the analysis due to incomplete data. Nonetheless, these markers were commonly elevated in available records and might be more reflective of ICU risk in this younger population. Recent studies have also begun to highlight that even previously healthy individuals can experience severe disease due to dysregulated immune responses, especially in regions with limited access to early diagnostics and interventions (33).

The current study revealed no statistically significant association between COVID-19 patients’ chief complaints and their rates of ICU admission or in-hospital mortality, which aligns with the findings of Weehuizen and Hoepelman (47). However, this lack of association should be interpreted with caution. It may be attributable to the relatively limited sample size, which restricts the statistical power needed to detect associations—particularly for symptoms with lower prevalence. Additionally, retrospective data collection may have led to underreporting or inconsistent documentation of presenting complaints, despite our efforts to supplement missing data through manual review.

Nonetheless, certain physiological indicators showed significant differences. Oxygen saturation (O₂ saturation, p = 0.040) and partial pressure of oxygen (PaO₂, p = 0.010) were both significantly lower among ICU-admitted patients, supporting their value as early markers of clinical deterioration. These findings are consistent with those of Sadeghi et al. (34), who emphasized the prognostic value of declining oxygen levels. Notably, “silent hypoxia”—characterized by severe hypoxemia without overt respiratory distress—has been documented in up to 30% of hospitalized COVID-19 cases and is strongly associated with poor outcomes (35).

Furthermore, blood urea nitrogen (BUN) levels were significantly associated with ICU admission (p = 0.017), supporting the findings of Küçükceran et al. (35). BUN reflects both renal function and catabolic stress, with elevated levels often seen in conditions like heart failure, infection-related hypercatabolism, and gastrointestinal bleeding (34). Similarly, white blood cell count (WBC) showed significant association (p = 0.032), consistent with observations by Sadeghi et al. (34). In COVID-19, leukocyte dysregulation—particularly the failure of monocytes and macrophages to contain SARS-CoV-2 replication—has been implicated in disease progression. The random blood sugar (RBS) levels also showed significant association with ICU admission (p = 0.002), echoing the results of Lotfy and Shama (36), who emphasized that severe COVID-19 can cause stress-induced hyperglycemia and pancreatic dysfunction (36).

Furthermore, blood urea nitrogen (BUN) levels were significantly associated with ICU admission (p = 0.017), supporting the findings of Küçükceran et al. (35). BUN reflects both renal function and catabolic stress, with elevated levels often seen in conditions such as heart failure, infection-related hypercatabolism, and gastrointestinal bleeding (34). Similarly, white blood cell count (WBC) showed a significant association (p = 0.032), consistent with observations by Sadeghi et al. (34), as leukocyte dysregulation—particularly the failure of monocytes and macrophages to contain SARS-CoV-2 replication—has been implicated in disease progression. Random blood sugar (RBS) levels also showed a significant association with ICU admission (p = 0.002), echoing the results of Lotfy and Shama (36), who emphasized that severe COVID-19 can trigger stress-induced hyperglycemia and pancreatic dysfunction.

Although elevated RBS was significantly associated with ICU admission, the strength and direction of this association varied across subgroups and analytic models. This may reflect the influence of factors such as steroid-induced hyperglycemia, unmeasured baseline glycemic control, or variability in timing of glucose measurement relative to disease progression. These complexities underscore the need for future studies that incorporate baseline glycemic status and treatment effects when evaluating the prognostic value of blood glucose in COVID-19 patients.

Importantly, both RBS and certain medications such as Actemra (tocilizumab) represent modifiable risk factors, and their associations with ICU admission warrant further exploration within a causal inference framework. Recent methodological advances, including target trial emulation (TTE), provide a structured approach to estimating causal effects from observational data by defining eligibility criteria, treatment strategies, follow-up windows, and outcome measures, mimicking a randomized controlled trial (37). Applying such methods—e.g., marginal structural models or inverse probability weighting—can help determine whether interventions targeting glycemic control or immunomodulation reduce ICU admissions. These tools are particularly relevant in real-world and resource-limited settings like Palestine, where randomized trials may be logistically or ethically challenging.

The presence of pneumonia was another key predictor, as patients with pneumonia were significantly more likely to be admitted to the ICU (p = 0.003), in line with findings from Sadeghi et al. (34) and Küçükceran et al. (35). COVID-19-related pneumonia contributes to acute respiratory distress syndrome (ARDS) and cardiovascular complications, which often necessitate critical care (48). In contrast, variables such as systolic/diastolic blood pressure, blood pH, and bicarbonate (HCO₃) levels were not significantly different between ICU and ward patients, contradicting studies like Ran et al. (38), which highlighted their prognostic relevance (34, 35, 38).

Antibiotic use presented a mixed picture. While vancomycin and meropenem usage were associated with higher ICU admission rates, Actemra (tocilizumab) appeared to have a protective effect, although only 13 patients received it. This result aligns with Minihan et al. (39), who noted that tocilizumab is beneficial when combined with corticosteroids in patients requiring oxygen support (39). However, the small sample size limits the strength of this conclusion. Antibiotics such as ceftriaxone and azithromycin did not show a significant association with ICU admission, consistent with Abu-Rub et al. (40), who noted that antibiotics are ineffective against viral infections unless complicated by bacterial coinfections (40).

Regarding corticosteroids, although dexamethasone was widely used and is known to reduce mortality in patients requiring respiratory support, the present study’s findings differ from those of Reyes et al. (41), who reported it as a risk factor for ICU admission (41). Obesity emerged as a significant predictor of ICU admission, which is in agreement with Singh et al. (42). The infiltration of SARS-CoV-2 into adipocytes likely contributes to an exaggerated inflammatory response and poorer outcomes in obese individuals (18). Conversely, Singh et al. (49) did not find obesity to be a significant predictor, highlighting variability across populations (42, 43).

The WBC and RBS levels were also confirmed as independent predictors of ICU admission (p = 0.005 and p = 0.014, respectively), supporting Sobhani et al. (50). These markers may reflect heightened immune and metabolic stress in severely ill patients. Notably, this study also observed an inverse association between higher BUN/RBS levels and ICU admission, a finding inconsistent with studies by Cerbu et al. (44), who identified them as strong predictors. These discrepancies may be due to patient heterogeneity or methodological differences (44).

Although vomiting was not statistically significant (p = 0.40), the adjusted odds ratio (OR = 1.508) suggested a possible trend toward worse outcomes. Moreover, obese patients exhibited significantly shorter ward stays prior to ICU transfer, consistent with Kim et al. (18), suggesting that obesity accelerates disease progression. On the contrary, Singh et al. (49) did not find obesity to impact ICU timing.

PaO₂ and BUN levels were also linked to earlier ICU admission, reinforcing the findings of Küçükceran et al. (35) and Sadeghi et al. (34). Uremic toxicity and oxygen desaturation act as early warning signs of decompensation (42). Similarly, RBS levels were associated with earlier ICU transfer, as glucose dysregulation, especially indiabetics, is known to exacerbate COVID-19 severity (36, 50).

Patients with pneumonia experienced earlier ICU admissions than those without pneumonia, consistent with Russo et al. (45). COVID-19-induced lung injury can precipitate rapid clinical decline. Pregnant women also had significantly shorter times to ICU transfer, in line with Kim et al. (18), likely due to physiological changes such as increased cardiac output and reduced pulmonary reserve (45).

Smokers were also admitted to ICU sooner than non-smokers, reflecting existing literature by Guan et al. (2) and Kim et al. (18). Smoking is linked to immune suppression and increased ACE2 expression, which facilitates viral entry. However, this contradicts findings from Maraqa et al. (51), who reported no significant association. Lastly, individuals with blood type A+ reached the ICU more quickly than those with O+, corroborating Halim et al. (20), who proposed that blood group antigens may influence coagulation and inflammatory pathways.

This study represents the first comprehensive investigation in Palestine specifically designed to identify the clinical, demographic, and treatment-related risk factors associated with ICU admission among hospitalized COVID-19 patients. It provides localized evidence tailored to the Palestinian healthcare context, which is crucial for guiding national health policy and resource allocation. By employing both logistic and Cox regression analyses, this study adds to the global understanding of COVID-19 severity predictors, particularly in low-resource and conflict-affected settings. The findings—especially those related to obesity, blood urea nitrogen (BUN), white blood cell (WBC) count, and random blood sugar (RBS)—can be immediately utilized by healthcare professionals to triage and prioritize care for high-risk patients.

However, the study also faced several limitations. Incomplete laboratory test data for some patients arose due to limited hospital capabilities; many analyses were performed externally and results were communicated informally (e.g., via WhatsApp). Missing or partial CT scan reports reduced the accuracy of radiological assessments. The exclusion of children and adolescents limits the generalizability of findings across all age groups. Furthermore, since the study focused solely on hospitalized adults, there is a risk of selection bias in estimating illness severity. Some patient files lacked key information such as clinical complaints, medical history, and vital signs, which reduced the robustness of certain statistical comparisons. Incomplete laboratory test data for some patients arose due to limited hospital capabilities, as many advanced analyses (e.g., CRP, ferritin, D-dimer, IL-6) were either unavailable or outsourced with results communicated informally. This missingness may not be random and could disproportionately affect patients from under-resourced hospitals, introducing potential selection bias. The absence of such critical biomarkers may have limited our ability to identify or confirm additional predictors of ICU admission. Furthermore, it may have attenuated the strength or statistical significance of certain associations. Future studies should ensure routine collection and centralized availability of key laboratory parameters to strengthen the predictive validity of ICU risk models.

The number of patients who received Tocilizumab (Actemra) was small (n = 13), which limited the study’s ability to evaluate its potential protective effects. In addition, political constraints—particularly Israeli-imposed delays and obstructions in the delivery of essential medical supplies, including ventilators—compromised the continuity of care and follow-up in critically ill patients. Time limitations also necessitated reliance on archived paper records for over two-thirds of the sample, except in Hugo Chavez Hospital where electronic data were available. Finally, the study cohort consisted entirely of moderate to critical cases, which limited variance in outcome comparisons.

Another important consideration is the inherent heterogeneity of COVID-19 patient populations. Our cohort included individuals with varied sociodemographic characteristics, comorbidities, and treatment exposures across six hospitals with differing capacities. Such diversity introduces potential effect modification and may explain inconsistencies with findings from other settings. Prior literature has emphasized that risk factors for ICU admission and outcomes can vary across subgroups defined by age, sex, comorbidity burden, or even ethnicity (46). Future studies using stratified analyses or interaction models—and ideally grounded in frameworks like subgroup-specific target trial emulation—are warranted to explore how clinical predictors vary across different patient populations. These approaches will enhance the precision and applicability of ICU triage strategies in diverse healthcare contexts.

Additionally, this retrospective study is subject to potential selection bias, as only hospitalized patients with complete records were included, possibly excluding individuals with milder disease managed in outpatient settings. Moreover, confounding by indication may have influenced observed associations between certain treatments (e.g., corticosteroids, immunomodulators) and ICU admission, since such interventions were often initiated based on clinical severity. While we adjusted for key confounding variables in multivariable models, residual or unmeasured confounding cannot be entirely ruled out.

Future research should include pediatric and adolescent populations to expand generalizability. Nationwide implementation of standardized electronic medical records is essential to reduce missing data and enhance the quality of retrospective analyses. Multicenter prospective studies are needed to validate the predictive models identified in this study. Routine availability of key laboratory markers (e.g., BUN, CRP, ferritin, d-dimer) and imaging data should be ensured in all treatment centers. Broader access to treatments such as Tocilizumab should be secured and studied in larger cohorts. International and local stakeholders must collaborate to remove barriers to the timely provision of essential medical equipment in Palestine. Finally, policymakers can use the findings from this study to develop early-warning systems or clinical scoring tools for ICU triage in resource-constrained settings.

In conclusion, the current study identifies key clinical, demographic, and biochemical predictors of ICU admission among hospitalized COVID-19 patients in the West Bank, Palestine. Obesity, abnormal WBC, BUN, and RBS levels, as well as pneumonia and low PaO₂, were significantly associated with both increased risk and earlier need for intensive care. These findings offer critical, context-specific insights that can guide evidence-based triage protocols, optimize resource allocation, and inform national health strategies in Palestine and similar low-resource settings.

Furthermore, our findings could establish a data base for new similar future pandemics and/or resurge of COVID-19 for better emergency preparedness and intervention measures mainly due to the risk of emergence of new variants and the decline in the surveillance system and wane herd immunity in the population which could re-emerge COVID-19 outbreaks.