Development and evaluation of predictive performance of “organ failure assessment by ratio score” to predict sepsis mortality in Indian intensive care unit patients: An observational study

Abstract

Background and Aims:

Sepsis is a common cause of mortality and morbidity in patients admitted to the intensive care unit (ICU), requiring reliable scoring systems to predict outcomes and guide management. Existing traditional scoring systems inadequately capture sepsis complexity. This study developed the Organ Failure Assessment by Ratio (OFAR) score to improve ICU mortality prediction in sepsis patients.

Methods:

This retrospective observational study was conducted in the ICUs of a tertiary care centre between January 2021 to December 2023 and included adult patients meeting Sepsis-3 criteria. Demographic data, ICU mortality, and six composite variables were collected: bilirubin-to-albumin ratio, pulse oximetric saturation to fraction of inspired oxygen ratio (SpO₂/FiO₂ ratio), shock index, lactate-to-bicarbonate ratio, urea-to-estimated glomerular filtration rate ratio, and cumulative lung ultrasound score-to-platelet ratio. These variables were evaluated to develop and assess the predictive accuracy of OFAR score for ICU mortality.

Results:

The OFAR score showed greater discriminatory power with an area under the curve (AUC) of 0.78 than quick Sequential Organ Failure Assessment score (qSOFA) (AUC: 0.70) and Acute Physiology and Chronic Health Evaluation score II (APACHE II) (AUC: 0.73) and was comparable to Sequential Organ Failure Assessment score (SOFA) (AUC: 0.76). With a cut-off score of 51.6, OFAR score demonstrated a sensitivity of 73.7% and a specificity of 72.1%.

Conclusion:

The OFAR score provides higher predictive power for ICU mortality than qSOFA and APACHE II in our study population, raising its potential as an alternative risk stratification scoring system in sepsis patients. Additional external validation is required to confirm its generalisability across diverse populations.

INTRODUCTION

Patients with sepsis are at high risk of morbidity and mortality in the intensive care unit (ICU). Early identification of high-risk patients and prompt initiation of treatment would prevent mortality in sepsis. Scoring systems help healthcare providers assess the severity, predict outcomes, and make informed decisions about treatment strategies. As the pathogenesis of sepsis is complex and multifactorial, the utility of contemporary severity-scoring systems in sepsis patients is limited.[1] Severity scores such as the Acute Physiology and Chronic Health Evaluation II (APACHE II), quick Sequential Organ Failure Assessment (qSOFA), and Sequential Organ Failure Assessment (SOFA) are commonly used to distinguish high-risk sepsis patients. Even if these traditional scores are relatively simple, because of a lack of precision, they may fail to capture patient-specific factors affecting outcomes of sepsis.[2]

Because of significant differences in population characteristics, pathogens, and clinical management capacity between high-income countries (HICs) and low- and middle-income countries (LMICs), the validity of these scoring systems in LMICs remains uncertain.[2] As the understanding of sepsis evolves, new clinical indicators are being discovered. Incorporating these into updated prediction models can improve the prediction of mortality by reflecting more recent scientific knowledge about the disease. This highlights the need for more pragmatic, accurate, flexible, and individualised mortality prediction scores that better account for the complexity of the condition, leverage modern technology, and incorporate new clinical insights.[3]

This study aimed to develop a new ICU mortality prediction score, termed Organ Failure Assessment by Ratio (OFAR) score, by using six measurable clinical ratios. The objectives were to assess the predictive accuracy of OFAR score for ICU mortality in patients with sepsis as defined by Sepsis-3 criteria and compare its discriminatory ability with established scoring systems, including APACHE II, qSOFA, and SOFA scores.

METHODS

This single-centre, retrospective observational study was conducted at a tertiary care centre in India. Informed consent was waived by the institutional review board as an anonymised retrospective dataset was obtained from existing health care records of sepsis-3 patients, admitted to surgical/medical ICUs, between January 2021 and December 2023 for one of our observational studies, for which approval from the institute ethics committee was secured (vide approval number IECPG-190/20.04.2023, RT-05/07.06.2023, dated: 07.06.2023). The study complied with the 2013 Declaration of Helsinki and good clinical practice guidelines.

Sepsis-3 definition (confirmed or suspected infection and SOFA score ≥ 2) was used to identify eligible patients.[4] We excluded patients younger than 18 years, those with cardiogenic or haemorrhagic shock, those who developed refractory septic shock before ICU admission, and those with incomplete data.

We extracted demographic, clinical, cumulative lung ultrasound score (CLUS) from point-of-care ultrasound (POCUS) and laboratory data of all eligible patients within 24 h of their ICU admission, along with outcome data (mortality) at the end of their ICU stay. The first 24 h of ICU admission represents a critical period for assessing sepsis severity and initiating therapeutic interventions. Variables were chosen from this time frame to maximise their predictive value for ICU mortality and align with standard clinical practice.

From ICU health care records of patients meeting Sepsis-3 criteria, we noted age, weight, sex, co-morbidities, heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO₂), fraction of inspired oxyen (FiO₂) requirement, systolic and diastolic blood pressure (SBP and DBP), platelet count, bilirubin, albumin, lactate, bicarbonate, urea, creatinine, CLUS, SOFA, APACHE- II, qSOFA scores [Table 1], and ICU mortality.

Table 1.

Component variables of SOFA, APACHE II, and qSOFA scores

Score	Parameters
SOFA	PaO2/FiO2 ratio, Platelet count, Bilirubin, Hypotension/vasopressor use, Glasgow Coma Scale (GCS), and Creatinine/urine output
APACHE II	Age, Temperature, Mean arterial pressure (MAP), Heart rate, Respiratory rate, Oxygenation (PaO2 or A–a gradient), Arterial pH, Serum sodium, Serum potassium, Serum creatinine, Haematocrit, White blood cell count, Glasgow Coma Scale (GCS), Chronic health conditions
qSOFA	Altered mentation (GCS <15), Systolic blood pressure ≤100 mmHg, Respiratory rate ≥22/min

Component variables of respective scoring systems. SOFA=Sequential Organ Failure Assessment; APACHE II=Acute Physiology and Chronic Health Evaluation II; qSOFA=quick Sequential Organ Failure Assessment; PaO₂/FiO₂=ratio of arterial oxygen tension to inspired oxygen fraction; MAP=mean arterial pressure; GCS=Glasgow Coma Scale

We calculated the following composite variables: (1) SpO₂/FiO₂ ratio, (2) bilirubin-to-albumin ratio,[5] (3) shock index (SI),[6] (4) serum lactate-to-bicarbonate ratio,[7] (5) urea-to-estimated glomerular filtration rate (eGFR) ratio,[8] and (6) CLUS-to-platelet ratio. The six composite variables each representing one major organ system were selected based on their clinical relevance to sepsis outcomes, as identified in prior literature, and based on their direct or indirect pathophysiological relevance to the sepsis.[7,8,9,10,11,12] Composite variables were prioritised due to their potential to provide a more holistic assessment of physiological derangements than individual parameters.

To ensure sufficient statistical power and minimise the risk of overfitting, we aimed for an events per variable (EPV) >30, which is approximately three times higher than the conventional recommendation of 10. With 12 variables in the model and 401 ICU mortality events, this corresponded to an EPV of 33, achieved by including 997 patients with complete data.[13] Variables with ≤25% of missing values were imputed by replacing them with the mean and mode. For continuous variables, Student’s t-test was used and presented as mean and standard deviation (SD). Categorical variables were analysed using the Chi-square test and are presented as counts and proportions. A P value <0.05 is considered statistically significant. For analysis, Stata Statistical Software: Release 16 (StataCorp LLC, College Station, Texas, USA) was used.

We analysed the composite variables in relation to the outcome, and the Youden index was employed to determine cut-off values that optimise sensitivity and specificity for predicting mortality. Dichotomous variables were made with the use of cut-off values. The variables were integrated into a logistic regression model for analysis. Correction factors were derived by rounding the odds ratios (ORs) obtained from logistic regression to integers to ensure clinical interpretability. Continuous composite variables were multiplied by their respective correction factors to generate the OFAR score [Figure 1a]. This approach allows integration of diverse physiological parameters into a unified scoring system. Discrimination was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC) for OFAR score, SOFA, qSOFA, and APACHE-II scores to compare their ability to predict ICU mortality.

Figure 1a.

Flow chart depicting the development of the OFAR score, including variable selection, dichotomisation, logistic regression modelling, derivation of correction factors, and final computation of the composite score. OFAR score = Organ Failure Assessment by Ratio Score; ORs=Odds ratios

RESULTS

The study initially assessed 1840 patients for eligibility, resulting in 997 participants, including 401 non-survivors and 596 survivors, with a mortality rate of 42.2% [Figure 1b]. Among the participants, 512 were female (51.35%), and 485 were male (48.65%). The study identified several significant differences in demographic and clinical variables between survivors and non-survivors. Increased age, female gender, and a higher Charlson Comorbidity Index (CCI) were associated with higher ICU mortality. Furthermore, non-survivors exhibited increased HR, RR, serum lactate, and creatinine levels, along with lower SpO₂. Additionally, higher qSOFA and SOFA scores, reflecting greater organ dysfunction, were associated with ICU mortality [Table 2]. Patients with hospital-acquired pneumonia (HAP) contribute the most to sepsis cases (35.81%) and ICU mortality (15.95%), followed by patients with community-acquired pneumonia (CAP) with 28.18% of cases and 11.13% mortality [Table 3]. All the six selected composite variables showed statistical significance as predictors of ICU mortality on univariable and multivariate analysis [Tables 4a and b].

Figure 1b.

Study flow chart illustrates the patient selection and analysis process

Table 2.

An overview of the baseline characteristics of all participants

Parameter		ICU Mortality		P
		Yes (401)	No (596)
Age (years)		47.43 (19.97)	44.55 (20.51)	0.027
Male		174 (43%)	311 (52.81%)	0.006
Female		227 (56%)	285 (47.81%)
Weight (kg)		65.39 (10.94)	64.39 (11.85)	0.172
Charlson Comorbidity Index		2.61 (2.32)	2.21 (2.29)	0.007
Temperature (°F)		98.45 (1.650	98.30 (1.197)	0.129
HR (beats/min)		114 (25.79)	102 (21.87)	<0.001
RR (breaths/min)		25 (6.04)	22.54 (5.42)	<0.001
SpO2 (%)		96.87 (4.60)	98.27 (2.14)	<0.001
SBP (mm of Hg)		118.25 (24.01)	118.22 (23.13)	0.986
FiO2 (%)		57.50 (22.77)	43.96 (15.97)	<0.001
Platelet count (cells/μL)		1.37 (1.15)	1.85 (1.25)	<0.001
Serum bilirubin (mg/dL)		2.9289 (3.64)	2.1288 (4.4058)	0.001
Serum lactate (mmol/L)		3.755 (3.60)	1.73 (1.54)	<0.001
Bicarbonate (mEq)		19.43 (7.11)	21.288 (6.21)	<0.001
Urea (mg/dL)		89.33 (61.61)	65.82 (58.88)	<0.001
Creatinine (mg/dL)		2.45 (2.15)	1.90 (2.14)	<0.001
CLUS (number)		18.16 (8.54)	14.11 (8.64)	<0.001
qSOFA		1.56 (0.79)	1.10 (0.75)	<0.001
SOFA		10.61 (4.08)	6.05 (3.22)	<0.001
APACHE-II		17.77 (7.07)	18.42 (7.54)	0.171
Dose of Vasopressors (μg/min)		12.11 (12.9)	4.07 (7.27)	0.001
Septic Shock	Yes	296 (29.6%)	227 (22.7%)	0.001
	No	104 (10.4%)	370 (37.1%)
Invasive Mechanical Ventilation	Yes	336 (33.7%)	323 (32.4%)	0.001
	No	64 (6.42)	274 (27.48%)
RRT	Yes	107 (10.73%)	126 (12.64%)	0.039
	No	293 (29.37%)	471 (47.24%)

Data presented as mean (standard deviation) for continuous variables and n (%) for categorical variables. HR=heart rate; RR=respiratory rate; SpO₂=peripheral oxygen saturation; SBP=systolic blood pressure; FiO₂=fraction of inspired oxygen; CLUS=cumulative lung ultrasound score; qSOFA=quick Sequential Organ Failure Assessment; SOFA=Sequential Organ Failure Assessment; APACHE II=Acute Physiology and Chronic Health Evaluation II; RRT=renal replacement therapy; ICU=intensive care unit

Table 3.

Analysis of ICU mortality based on sepsis sources

Source of sepsis	ICU mortality		Total	P
	Yes	No
CAP	111 (11.13%)	170 (17.05%)	28.18%	0.212
HAP	159 (15.95%)	198 (19.86%)	35.81%
Perforation Peritonitis	34 (3.31%)	49 (5.02%)	8.32%
Acute Febrile illness	37 (3.71%)	66 (6.62%)	10.33%
Other source of infection	60 (6.02%)	113 (11.33%)	17.35%

Data presented as number (%). P<0.05 considered statistically significant. CAP=community-acquired pneumonia; HAP=hospital-acquired pneumonia; ICU=intensive care unit

Table 4a.

Univariable logistic regression analysis of selected composite variables

Composite Variables	OR (95%CI)	P
Total Bil/albumin Ratio	0.80 (0.73-0.89)	0.001
SpO2/FiO2 ratios	1.01 (1-1.01)	<0.001
SI	0.51 (0.35-0.75)	<0.001
Lactate/Bicarbonate Ratio	0.0087 (0.0031-0.0245)	<0.001
Urea/GFR Ratio	0.96 (0.94-0.98)	0.001
CLUS/Platelet count Ratio	0.99 (0.98-0.99)	<0.001

Data presented as odds ratio (OR) with 95% confidence interval (CI). P<0.05 considered statistically significant. SpO₂/FiO₂=ratio of peripheral oxygen saturation to inspired oxygen fraction; Shock index=heart rate/systolic blood pressure; CLUS=cumulative lung ultrasound score; GFR=glomerular filtration rate; SI=Shock Index; Bil=bilirubin

Table 4b.

Multivariable logistic regression analysis of composite variables

Variable (Cut-off)	OR (95%CI)	P
Total Bil/albumin Ratio (0.46)	1.44 (1.07-1.94)	0.014
SpO2/FiO2 ratio (240)	2.34 (1.76-3.12)	<0.001
SI (0.93)	1.51 (1.13-2.03)	0.005
Lactate/Bicarbonate Ratio (0.09)	2.77 (2.07-3.72)	<0.001
Urea/GFR Ratio (1.0)	2.62 (1.96-3.50)	<0.001
CLUS/Platelet count Ratio (10.62)	1.67 (1.25-2.23)	0.001

Cut-off for each composite variable is given in brackets. Data presented as odds ratio (OR) with 95% confidence interval (CI). P<0.05 considered statistically significant. SpO₂/FiO₂=ratio of peripheral oxygen saturation to inspired oxygen fraction; Shock Index (SI) = heart rate/systolic blood pressure; CLUS=cumulative lung ultrasound score; GFR=glomerular filtration rate; Bil=bilirubin

The six composite variables were used to calculate OFAR score: 10× bilirubin-to-albumin ratio +23.11 × SpO₂/FiO₂ ratios + 11.3 × SI + 27.7 × serum lactate-to-bicarbonate ratio + 26.2 × urea-to-eGFR ratio + 13.9 × CLUS-to-platelet ratio. According to receiver operating characteristic (ROC) curve analysis, the optimal OFAR score cut-off to predict ICU mortality was determined to be >51.6. A sensitivity of 73.7%, a specificity of 72.1%, and a positive likelihood ratio of 2.5 were observed. The AUC-ROC was 0.78 (95% confidence interval, 0.73–0.81) [Figure 2a].

Figure 2a.

Receiver operating characteristic curve showing the sensitivity and specificity of the model for predicting intensive care unit mortality among sepsis patients; the diagonal line represents the reference line for no discrimination (AUC-ROC 0.78)

The OFAR score demonstrated good discrimination with an area under curve (AUC) of 0.78, outperforming the APACHE-II and qSOFA scores [Figure 2b]. Additionally, its performance was comparable to that of the SOFA score.

Figure 2b.

Comparison of receiver operating characteristic (ROC) curves of the OFAR score, SOFA score, qSOFA score, and APACHE II score for prediction of ICU mortality; the diagonal line represents the reference line for no discrimination. SOFA = Sequential Organ Failure Assessment; APACHE II = Acute Physiology and Chronic Health Evaluation II; qSOFA = quick Sequential Organ Failure Assessment; OFARS = Organ Failure Assessment by Ratio Score

DISCUSSION

This retrospective observational study was intended to develop and evaluate OFAR score, to predict ICU mortality in sepsis-3 patients. By integrating additional clinical and laboratory parameters, particularly the six composite variables such as the bilirubin-to-albumin ratio, SpO₂/FiO₂ ratio, CLUS-to-platelet ratio, and lactate-to-bicarbonate ratio, we intended to enhance the predictive accuracy for sepsis-related mortality. The composite variables used in this study are grounded in established pathophysiological mechanisms. The SpO₂/FiO₂ ratio is a validated marker of hypoxaemia severity, commonly used in acute respiratory distress syndrome (ARDS) studies. Similarly, the lactate-to-bicarbonate ratio reflects the balance between tissue hypoperfusion and metabolic compensation, which is crucial in sepsis. While not all combinations have been extensively validated, this study offers an opportunity to assess their utility collectively in sepsis outcomes. This study included 997 patients, and ICU mortality was recorded in 42.2% of cases. The high ICU mortality is due to severe illness, as reflected in high SOFA and qSOFA scores, organ dysfunction markers, and a higher incidence of septic shock. Non-survivors required invasive interventions like mechanical ventilation and renal replacement therapy, indicating severity of illness; additionally, older age, co-morbidities, and HAP, which are linked to higher mortality, contributed significantly to the poor outcomes in this study population. Similar to the previous studies, the current study results demonstrated that several clinical parameters, including HR, RR, and serum lactate, were significantly elevated in non-survivors, reflecting their role in sepsis severity and prognosis.[1,14,15] Notably, our multivariate analysis identified these six composite variables as independent predictors of ICU mortality. The OFAR scoring system, developed with these predictors, showed fair discriminatory power with an AUC of 0.78, demonstrating enhanced predictive performance in comparison to the commonly used qSOFA and APACHE-II scores, and was comparable to SOFA score. While qSOFA is designed as a quick bedside tool, it has been criticised for its limited sensitivity in ICU settings.[16] APACHE II, though comprehensive, may not be specific to the sepsis population as it is a general severity-of-illness score.[17] SOFA, on the other hand, showed similar predictive ability to OFAR score. However, the integration of novel composite variables in OFAR score, such as the lactate-to-bicarbonate and CLUS-to-platelet ratios, offers a more tailored approach to sepsis, accounting for both organ dysfunction and haemodynamic instability. These novel metrics enhance the score’s ability to reflect the underlying pathophysiology of sepsis. With an OFAR score cut-off of 51.6, which yields a sensitivity of 73.7% and a specificity of 72.1%, it could serve as a reliable tool to recognise high-risk sepsis patients in the ICU.

We analysed new composite variables, urea-to-eGFR ratio and CLUS-to-platelet ratio; the biological plausibility for choosing these was explainable. Creatinine is the base for diagnosis and prognosis of sepsis-related mortality. When urea-to-creatinine ratio is used, it incorporates the pitfalls of using creatinine.[18] As absolute creatinine values cannot represent the exact severity of illness and organ dysfunction, eGFR will be the better representation of the organ function, making the urea-to-eGFR ratio a better representation than the urea-to-creatinine ratio.[18] The pathophysiological basis for CLUS-to-platelet ratio in sepsis is associated with inflammation and the inflammation alters the glycocalyx, which leads to adhesion and activation of platelets, resulting in neutrophil entrapment and inflammatory mediator release and increased capillary leaking, and leads to oedema in the lung and thrombocytopenia.[15,19,20,21] We assumed the severity of pulmonary oedema may correlate with severity of thrombocytopenia. We formulated a new composite variable, CLUS-to-platelet ratio. The remaining four composite variables were used to predict overall mortality in sepsis, rather than ICU-specific mortality. In this current study, all the six composite variables significantly predicted the ICU mortality individually and collectively as OFAR score.

Eliminating the potential disadvantages of majority of risk prediction scores, OFAR score is the simple and easy score which predicts the ICU mortality. Predictive performance was comparable to most of the risk prediction scores in sepsis. It outperformed qSOFA and APACHE-II, similar to SOFA score. Our OFAR score outperformed APACHE II due to its sepsis-specific composite variables, focus on metabolic and organ dysfunctions. APACHE II was not tailored for ICU mortality prediction in sepsis. A higher SOFA score correlates with greater mortality, yet it has limitations, including low discriminatory power and a limited range of variables result in a reported AUC ranging from 0.69 to 0.83 for predicting mortality in septic patients.[22,23,24] However, it was not designed to predict ICU mortality in sepsis.[25] Calculation of the SOFA score is confusing when the patients were intubated and on multiple vasopressors. Overcoming these drawbacks, we developed the OFAR score which included more pathophysiologically explainable variables of sepsis. While the SOFA score is widely used, the OFAR score incorporates additional composite variables, potentially offering greater sensitivity in identifying at-risk patients. Although more complex, the OFAR score demonstrated comparable predictive accuracy (AUC-ROC 0.78) and could serve as an adjunct tool for stratifying sepsis severity in ICU settings.

Metabolic derangement was the main underlying consequence of sepsis pathophysiology; representation of the component was lacking in the majority of risk scores. High lactate is associated with metabolic acidosis, which leads to high mortality in sepsis patients; to represent these components, we included lactate-to-bicarbonate ratio in the OFAR score.[1,26] Sepsis significantly impacts metabolic functions, often resulting in elevated serum lactate and reduced bicarbonate levels[27]. Lactate-to-bicarbonate ratio has a good discrimination ability to predict mortality in sepsis in our study and in dengue shock syndrome.[7] The OFAR score, based on easily available clinical and laboratory data of initial 24 h of admission, offers a practical and effective tool for early risk stratification. This allows clinicians to prioritise resources and interventions for high-risk patients. Additionally, the use of bedside POCUS to calculate the CLUS-to-platelet ratio highlights the growing importance of non-invasive monitoring in sepsis management. The development of composite ratios such as lactate-to-bicarbonate and CLUS-to-platelet enhances the precision of the score by accounting for metabolic derangements and pulmonary involvement in sepsis. These components could potentially aid in more personalised therapeutic strategies, including fluid management, respiratory support, and early intervention in cases of metabolic acidosis.

The retrospective design of this study poses the potential biases in data extraction and interpretation. This can introduce inaccuracies as certain variables of individual patients were poorly documented. Since this study was conducted at a single centre, the generalisability of the findings may be constrained. The medical care practices and patient populations in this institution may not reflect those in other hospitals or regions, making it difficult to apply the conclusions universally. An important limitation is the absence of external validation for the composite variables, included in the OFAR score, as they have not been validated in patients with sepsis. Moreover, missing data led to patient exclusion, introducing selection bias and potentially skewed findings. Mean or mode imputation may further reduce accuracy as it oversimplifies clinically complex variables and may fail to represent true variability.

The study population was limited to patients diagnosed with sepsis, based on Sepsis-3 criteria, within the first 24 h of ICU admission. This narrow focus excluded patients diagnosed with sepsis after 24 h of ICU admission, thus omitting a potentially significant portion of the sepsis population. The exclusion of key variables such as prothrombin time, calcium, and body mass index, factors that may be related to sepsis-related mortality, further limits the comprehensiveness of the analysis. Lastly, the study’s use of composite variables, such as the CLUS-to-platelet and lactate-to-bicarbonate ratios, while innovative, may complicate the clinical application of the OFAR score. These variables, though statistically significant, could be challenging to interpret or apply in real-world settings. The study’s reliance on early ICU data and the absence of an assessment of long-term outcomes or different therapeutic interventions further limit its practical impact on sepsis management.

CONCLUSION

This study introduces the OFAR score, which integrates composite variables derived from commonly available clinical and laboratory parameters. By capturing complex interactions between organ systems, the score offers a novel perspective for risk stratification in patients with sepsis. Its applicability in guiding treatment decisions or identifying patients for clinical trials warrants further validation in multicentre studies.

Presentation at conferences/CMEs and abstract publication

The preliminary abstract of the study was presented at IARS 2025, held in Hawaii, and the abstract was published in the IARS and SOCCA Annual Meetings Programme on 20.03.2025. Abstracts of International Anesthesia Research Society and Society of Critical Care Anesthesiologists 2025 Annual Meetings. Anesth Analg 2025;140(5S):1-1247. | doi: 10.1213/ANE.0000000000007736

Study data availability

De-identified data may be requested with reasonable justification from the authors (email to the corresponding author) and shall be shared after approval as per the authors’ institution’s policy.

Disclosure of use of artificial intelligence (AI)-assistive or generative tools

The AI tools or language models (LLM) have not been utilised in the manuscript, except that software has been used for grammar corrections and references.

Declaration of use of permitted tools

All figures and tables are the author’s own work.

Supplementary material

Nil.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.