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Sains Malaysiana 55(4)(2026): 756-773
http://doi.org/10.17576/jsm-2026-5504-14
Bayesian
Logistic Regression to Explore the Role of Complete Blood Count in Kidney
Disease Mortality
(Regresi Logistik Bayesian untuk Meneroka Peranan Kiraan Darah Lengkap dalam Kematian Penyakit Ginjal)
DG SITI
NURISYA SAHIRAH AG ISHA1, NURLIYANA JUHAN2,*,
YONG ZULINA ZUBAIRI3, NORNAZIRAH AZIZAN4, CHONG MUN HO1,
ABU SAYED MD. AL MAMUN5 & QIN ZHI LEE6
1Faculty
of Science and Technology, Universiti Malaysia Sabah,
Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
2Preparatory
Centre for Science and Technology, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
3Institute
for Advanced Studies, University of Malaya, 50603 Kuala Lumpur, Malaysia
4Faculty
of Medicine and Health Sciences, Universiti Malaysia
Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
5Department
of Statistics, University of Rajshahi, Rajshahi, Bangladesh
6Queen
Elizabeth Hospital, Locked Bag No. 2029, 88586 Kota Kinabalu, Sabah, Malaysia
Received:
4 July 2025/Accepted: 20 April 2026
Abstract
Kidney disease is a major global health
challenge, ranking fifth in Malaysia and ninth worldwide as a leading cause of
death in 2021. This growing burden highlights the need for cost-effective tools
to support early identification of patients at risk of mortality. The complete
blood count (CBC) is an affordable, widely used diagnostic test, while Bayesian
methods offer advantages for incorporating prior knowledge and quantifying
uncertainty. However, the use of CBC parameters with Bayesian approaches for
mortality prediction among kidney disease patients in Malaysia remains limited.
This study aimed to develop a risk stratification model for kidney disease
mortality using CBC data and Bayesian logistic regression (BLR). A
retrospective study was conducted using data from 5,158 patients with kidney
disease treated at Queen Elizabeth I Hospital. The final multivariate BLR model
identified 13 significant predictors of mortality. The strongest predictors
were low haemoglobin, high mean platelet volume (MPV), and high
neutrophil-to-lymphocyte ratio (NLR), followed by high white blood cells (WBC),
and hospitalisation history. The model demonstrated good calibration and
discrimination, with an area under the receiver operating characteristic curve
(AUROC) and area under the precision-recall curve (AUPRC) greater than 0.8,
supporting its reliability for mortality risk stratification. These findings
suggest that combining CBC parameters with demographic information may improve
early detection and clinical decision-making, particularly in resource-limited
settings.
Keywords: Bayesian logistic regression; complete
blood count; kidney disease; mortality; risk stratification
Abstrak
Penyakit buah pinggang merupakan cabaran kesihatan global yang utama, menduduki
tempat kelima di Malaysia dan kesembilan di peringkat dunia sebagai penyebab
utama kematian pada tahun 2021. Beban yang semakin meningkat ini menekankan
keperluan terhadap alat diagnostik yang kos efektif untuk memudahkan pengesanan
awal. Kiraan darah lengkap (KDL) merupakan ujian diagnostik yang berpatutan dan
digunakan secara rutin, manakala kaedah Bayesian menawarkan kelebihan dengan
menggabungkan pengetahuan terdahulu dan memberikan pengkuantitian ketakpastian
yang lebih tepat. Walau bagaimanapun, penggunaan parameter KDL dan Bayesian bagi
meramal kematian dalam kalangan pesakit buah pinggang di Malaysia masih terhad.
Kajian ini bertujuan untuk membangunkan model stratifikasi risiko bagi kematian
akibat penyakit buah pinggang menggunakan KDL dan regresi logistik Bayesian
(BLR). Kajian retrospektif ini menganalisis data daripada 5,158 pesakit buah
pinggang dari Hospital Queen Elizabeth I. Model BLR multivariat akhir mengenal
pasti 13 peramal kematian yang signifikan. Peramal yang paling kuat ialah hemoglobin rendah, min isi padu platelet (MPV) yang tinggi dan nisbah neutrofil kepada limfosit (NLR) yang tinggi, diikuti oleh
sel darah putih (WBC) yang tinggi dan sejarah kemasukan ke hospital. Model
menunjukkan prestasi penentukuran dan diskriminasi yang baik, dengan kawasan
bawah lengkungan dalam menerima ciri operasi (AUROC) dan kawasan di bawah
lengkung penarikan balik ketepatan (AUPRC) melebihi 0.8, sekali gus menyokong
kebolehpercayaannya untuk stratifikasi risiko kematian. Penemuan ini mencadangkan
bahawa penggabungan parameter KDL dengan maklumat demografi berpotensi meningkatkan
pengesanan awal pesakit berisiko dan keputusan klinikal, khususnya dalam
persekitaran dengan sumber terhad.
Kata kunci: Bayesian regresi logistik; kiraan darah lengkap; kematian; penyakit buah pinggang; stratifikasi risiko
REFERENCES
Agnello, L., Giglio, R.V., Bivona, G., Scazzone, C.,
Gambino, C.M., Iacona, A., Ciaccio, A.M., Lo Sasso, B. & Ciaccio, M. 2021. The value of a complete blood count (CBC) for
sepsis diagnosis and prognosis. Diagnostics 11(10): 1881.
Arif, F.M., Sumida, K., Molnar, M.Z., Potukuchi, P.K.,
Lu, J.L., Hassan, F., Thomas, F., Siddiqui, O.A., Gyamlani,
G.G., Kalantar-Zadeh, K. & Kovesdy,
C.P. 2017. Early mortality associated with inpatient versus outpatient hemodialysis initiation in a large cohort of US veterans
with incident end-stage renal disease. Nephron 137(1): 15-22.
Aw, J.Y.H., Clarke, N.E., Mayfield, H., Lau, C.L.,
Richardson, A. & Nery, S.V. 2021. Novel statistical approaches to identify
risk factors for soil-transmitted helminth infection in Timor-Leste. International
Journal for Parasitology 51(9): 729-739.
Balasa, M.J. 2023. Accuracy of platelet indices as
predictor of in-hospital mortality in patients with acute kidney injury
requiring renal replacement therapy. Nephrology Dialysis Transplantation 38(Suppl 1).
Binuya, M.A.E., Engelhardt, E.G., Schats, W., Schmidt,
M.K. & Steyerberg, E.W. 2022. Methodological guidance for the evaluation
and updating of clinical prediction models: A systematic review. BMC Medical
Research Methodology 22(1): 316.
Bui, M., Groothuis-Oudshoorn, C.G.M., Witteveen, A.
& Hegeman, J.H. 2024. Palliative non-operative management in geriatric hip
fracture patients: When would surgeons abstain from surgery? Journal of
Clinical Medicine 13(6): 1594.
Bürkner, P.C. 2017. brms: An R package for Bayesian
multilevel models using Stan. Journal of Statistical Software 80(1): 1-28.
Bürkner, P.C., Gabry, J., Weber, S., Johnson, A.,
Modrak, M., Badr, H.S., Weber, F., Vehtari, A.,
Ben-Shachar, M.S., Rabel, H., Mills, S.C., Wild, S., Popov, V., Kosmidis, I.,
Schneider, B., Kallioinen, N., Silva, W.J., Carvalho,
L. & Pekin, S. 2024. brms: Bayesian
regression models using 'Stan' (R package version 2.22.0). CRAN.
Çeleğen, K. & Çeleğen, M. 2024. Prognostic significance of mean
platelet volume to platelet count ratio in pediatric patients with acute kidney injury. The Turkish Journal of Pediatrics6(3): 354-363.
Chicco, D., Tötsch, N. & Jurman, G. 2021. The
Matthews correlation coefficient (MCC) is more reliable than balanced accuracy,
bookmaker informedness, and markedness in two-class confusion matrix
evaluation. BioData Mining 14.
Dean, L. 2005. Blood Groups and Red Cell Antigens [Internet]. Bethesda (MD): National Center for
Biotechnology Information (US).
Depaoli, S. & van de Schoot, R. 2017. Improving
transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychological
Methods 22(2): 240-261.
Dratch, A., Kleine, C.E., Streja, E., Soohoo, M.,
Park, C., Hsiung, J.T., Rhee, C.M., Obi, Y., Molnar, M.Z., Kovesdy, C.P. & Kalantar-Zadeh,
K. 2019. Mean corpuscular volume and mortality
in incident hemodialysis patients. Nephron 141(3): 188-200.
Drăgoescu, A.N., Pădureanu, V.,
Stănculescu, A.D., Chiuțu, L.C., Tomescu, P., Geormăneanu,
C., Pădureanu,
R., Iovănescu, V.F., Ungureanu,
B.S., Pănuș, A. & Drăgoescu,
O.P. 2021. Neutrophil to lymphocyte ratio (NLR) -
A useful tool for the prognosis of sepsis in the ICU. Biomedicines 10(1):
75.
Egidi, L., Pauli, F. & Torelli, N. 2022. Avoiding
prior–data conflict in regression models via mixture priors. Canadian
Journal of Statistics 50(2): 491-510.
Fan, F., Jia, J., Li, J., Huo, Y. & Zhang, Y.
2017. White blood cell count predicts the odds of kidney function decline in a
Chinese community-based population. BMC Nephrology 18(1): 190.
Fanconi, C., de Hond, A., Peterson, D., Capodici, A.
& Hernandez-Boussard, T. 2023. A Bayesian approach to predictive
uncertainty in chemotherapy patients at risk of acute care utilization. EBioMedicine 92: 104632.
Farhin, S. & Ali Khan, A. 2022. Bayesian survival
analysis of acute encephalitis syndrome with censoring mechanism using brms
package. Journal of Statistics Applications & Probability 11(3):
963-980.
Gabry, J. 2024. loo: Efficient leave-one-out cross-validation and WAIC for
Bayesian models (Version 2.8.0) [R package]. https://cloud.r- project.org/web/packages/loo/loo.pdf
Gameiro, J. & Lopes, J.A. 2019. Complete blood
count in acute kidney injury prediction: A narrative review. Annals of
Intensive Care 9: 87. https://doi.org/10.1186/s13613-019-0561-4
Ganapathy, S., Harichandrakumar, K.T., Penumadu, P.,
Tamilarasu, K. & Nair, N.S. 2022. Comparison of Bayesian, frequentist and
machine learning models for predicting the two-year mortality of patients diagnosed
with squamous cell carcinoma of the oral cavity. Clinical Epidemiology and
Global Health 17: 101145.
Gelman, A., Jakulin, A., Pittau, M.G. & Su, Y.S.
2008. A weakly informative default prior distribution for logistic and other
regression models. The Annals of Applied Statistics 2(4): 1360-1383.
Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B.,
Vehtari, A. & Rubin, D.B. 2025. Bayesian Data Analysis. 3rd ed. New
York: Chapman and Hall.
Gerber, G. & Scharler, U.M. 2024. Jump around:
Selecting Markov Chain Monte Carlo parameters and diagnostics for improved food
web model quality and ecosystem representation. Ecological Informatics 84: 102865.
Gosho, M., Ishii, R., Nagashima, K., Noma, H. &
Maruo, K. 2025. Determining the prior mean in Bayesian logistic regression with
sparse data: A nonarbitrary approach. Journal of the Royal Statistical
Society: Series C (Applied Statistics) 74(1): 126-141.
Gunn-Sandell, L.B., Bedrick, E.J., Hutchins, J.L.,
Berg, A.A., Kaizer, A.M. & Carlson, N.E. 2024. A practical guide to
adopting Bayesian analyses in clinical research. Journal of Clinical and
Translational Science 8(1): e3.
Guo, S., Yang, L., Zhu, X., Zhang, X., Sun, Z., Meng,
L., Wang, Y., Li, J., Cheng, S., Zhuang, X. & Cui, W. 2024. Risk factors of
different mortality periods in
older patients with end-stage renal disease undergoing urgent-start peritoneal
dialysis: A retrospective observational study. BMC Geriatrics 24(1): 343.
Han, W., Allameen, N.A., Ibrahim, I., Dhanasekaran,
P., Mengling, F. & Lahiri, M. 2022. Hospital admission risk stratification
of patients with gout presenting to the emergency department. Clinical
Rheumatology 41(6): 1801-1807. https://doi.org/10.1007/s10067-021-05902-5
Hashmi, M.F., Benjamin, O. & Lappin, S.L. 2023. End-Stage
Renal Disease [updated 2023 Aug 28].
In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing.
Hoffman, M.D. & Gelman, A. 2014. The No-U-turn
sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. arXiv [Preprint].
Huang, Y., Li, W., Macheret, F., Gabriel, R.A. &
Ohno-Machado, L. 2020. A tutorial on calibration measurements and calibration
models for clinical prediction models. Journal of the American Medical
Informatics Association 27(4): 621-633.
Huynh, Q.T.V., Le, N.Q.K., Huang, S.Y., Ho, B.T., Vu,
T.H., Pham, H.T.M., Pham, A.L., Hou, J.W., Nguyen, N.T.K. & Chen,
Y.C. 2022. Development and validation of
clinical diagnostic model for girls with central precocious puberty:
Machine-learning approaches. PLOS ONE 17(1): e0261965.
Jeon, Y.H., Jeon, Y., Jung, H.Y., Choi, J.Y., Park,
S.H., Kim, C.D., Kim, Y.L., Cho, J.H. & Lim, J.H. 2023. Platelet-to- lymphocyte
ratio and in-hospital mortality in patients with AKI receiving continuous kidney replacement therapy: A retrospective
observational cohort study. Kidney Medicine 5(6): 100642.
Johnson, A.A., Ott, M.Q. & Dogucu, M. 2022. Bayes Rules!: An Introduction to Applied Bayesian Modeling. 1st ed. Boca Raton: Chapman and Hall/CRC.
Juhan, N., Zubairi, Y.Z., Khalid, Z.M. & Zuhdi,
A.S.M. 2020a. A comparison between Bayesian and frequentist approach in the
analysis of risk factors for female cardiovascular disease patients in
Malaysia. ASM Science Journal
Juhan, N., Zubairi, Y.Z., Khalid, Z.M. & Mahmood
Zuhdi, A.S. 2020b. Risk factors of mortality among male patients with
cardiovascular disease in Malaysia using Bayesian analysis. Iranian Journal
of Public Health 49(9): 1642-1649.
Khan, M.I. & Ullah, I. 2020. Diagnostic importance
of mean platelet volume, platelet distribution width and platelet large cell
ratio as screening tool in immune thrombocytopenia. Porto Biomedical Journal 5(6): e094.
Kim, M., Kym, D., Park, J., Yoon, J., Cho, Y.S., Hur,
J., Chun, W. & Yoon, D. 2024a. Big data insights into the diagnostic values
of CBC parameters for sepsis and septic shock in burn patients: A retrospective
study. Scientific Reports 14: 800.
Kim, Y.H., Lee, W., Kim, K.Y., Kim, Y., Ko, A., Weon,
B., Lee, J., Jin, W., Kim,
D.K., Kim, Y.S., Lim, C.S., Lee, J.P.; Korean Association for the study of
Renal Anemia, artificial Intelligence (KARAI) 2024b. The estimated mediating roles of anemia-related
variables in the association between kidney function and mortality: A National
Health and Nutrition Examination Survey (NHANES) study. Scientific Reports 14(1): 6621.
Kimura, H., Tanaka, K., Saito, H., Iwasaki, T.,
Kazama, S., Shimabukuro, M., Asahi, K., Watanabe, T. & Kazama, J.J. 2023. Impact
of red blood cell distribution width–albumin ratio on prognosis of patients
with CKD. Scientific Reports 13(1): 15774.
Kor, C.T., Hsieh, Y.P., Chang, C.C. & Chiu, P.F.
2018. The prognostic value of interaction between mean corpuscular volume and
red cell distribution width in mortality in chronic kidney disease. Scientific
Reports 8(1): 11870. https://doi.org/10.1038/s41598-018-19881-2
Kruschke, J.K. 2021. Bayesian analysis reporting
guidelines. Nature Human Behaviour 5(10): 1282-1291.
Larson, C., Kaplan, D., Girolamo, T., Kover, S.T.
& Eigsti, I.M. 2023. A Bayesian statistics tutorial for clinical research:
Prior distributions and meaningful results for small clinical samples. Journal
of Clinical Psychology 79(11): 2602-2624.
Lemoine, N.P. 2019. Moving beyond noninformative
priors: Why and how to choose weakly informative priors in Bayesian analyses. Oikos 128(7): 912-928.
Li, D., Wang, A., Li, Y., Ruan, Z., Zhao, H., Li, J., Zhang,
Q. & Wu, B. 2024. Nonlinear relationship of red blood cell indices (MCH,
MCHC, and MCV) with all-cause and cardiovascular mortality: A cohort study in
U.S. adults. PLoS ONE 19(8): 1-13.
Liao, J., Wei, D., Sun, C., Yang, Y., Wei, Y. &
Liu, X. 2022. Prognostic value of the combination of neutrophil-to-lymphocyte
ratio, monocyte-to-lymphocyte ratio and platelet-to-lymphocyte ratio on
mortality in patients on maintenance hemodialysis. BMC Nephrology 23(1):
393.
Lindhiem, O., Petersen, I.T., Mentch, L.K. &
Youngstrom, E.A. 2018. The importance of calibration in clinical psychology. Assessment 27(4): 840-854.
Lindholm, B., Zhang, X., Chen, Z., Qureshi, A.R.T.,
Danielson, K., Mukai, H., Suliman, M., Heimbürger,
O., Chen, J., Stenvinkel, P., Barany, F.P. 2022. MO673:
Association of mean corpuscular volume with mortality in kidney failure patients: Opposite findings in Chinese and
Swedish kidney failure patients. Nephrology Dialysis Transplantation 37(Suppl 3): gfac078.010.
Lukman, P.A., Abdullah, S. & Rachman, A. 2021.
Bayesian logistic regression and its application for hypothyroid prediction in
post-radiation nasopharyngeal cancer patients. Journal of Physics:
Conference Series 1725(1): 012010.
https://doi.org/10.1088/1742-6596/1725/1/012010
Luo, J., Zhou, Y., Song, Y., Wang, D., Li, M., Du, X.,
Kang, J., Ye, P. & Xia, J. 2024. Association between the
neutrophil-to-lymphocyte ratio and in-hospital mortality in patients with
chronic kidney disease and coronary artery disease in the intensive care unit. European
Journal of Medical Research 29(1): 260.
Ma, J., Vongpradith, A., Ledesma, J.R., Novotney, A.,
Yi, S., Lim, K., Hay, S.I., Murray, C.J.L. & Kyu, H.H. 2022. Progress towards the 2020 milestones of the End
TB Strategy in Cambodia: Estimates of age- and sex-specific TB incidence and
mortality from the Global Burden of Disease Study 2019. BMC Infectious
Diseases 22(1): 904.
Ma, L., Han, Q., Sun, F., Zhu, K. & Sun, Q. 2023.
Mean platelet volume/platelet count ratio as a predictor of both incidence and
prognosis of acute ischemic stroke in hemodialysis patients. International Journal of General Medicine 16: 3985-3994.
Mahachai, N., Washirasaksiri, C., Ariyakunaphan, P., Kositamongkol, C.,
Sitasuwan, T., Tinmanee, R., Auesomwang,
C., Sayabovorn, N., Chaisathaphol,
T., Phisalprapa, P., Charatcharoenwitthaya,
P. & Srivanichakorn, W. 2023. Clinical predictive score for identifying metabolic
dysfunction-associated steatotic liver disease in individuals with prediabetes
using transient elastography. Journal of Clinical Medicine 12(24): 7617.
Marine, B.T. & Zewde, M.G. 2024. Modern
contraceptive utilization among street beggar women in the Sidama region of Ethiopia using Bayesian model approach. Reproductive Health 21: 163.
Mattos, D.I. & Ramos, É.M.S. 2022. Bayesian paired
comparison with the bpcs package. Behavior Research Methods 54(4): 2025-2045.
https://doi.org/10.3758/s13428-021-01714-2
Mohd Faizal, A.S., Hon, W.Y., Thevarajah,
T.M., Khor, S.M. & Chang, S-W. 2023. A biomarker discovery of acute
myocardial infarction using feature selection and machine learning. Medical
& Biological Engineering & Computing 61(10): 2527-2541.
Nguyen, L.P., Tung, D.D., Nguyen, D.T., Le, H.N.,
Tran, T.Q., Binh, T.V. & Pham, D.T.N. 2023. The utilization of machine
learning algorithms for assisting physicians in the diagnosis of diabetes. Diagnostics 13(12): 2087.
Oh, E.S., You, Z., Nowak, K.L. & Jovanovich, A.J.
2022. Association of monocyte count and monocyte/lymphocyte ratio with the risk
of cardiovascular outcomes in patients with CKD. Kidney360 3(4): 657-665.
Omar, M.S.F., Md Noor, S., Seman, Z. & Mohd
Tajuddin, S.N. 2023. Full blood count parameters in COVID-19 patients with
disease severity, patient outcome and vaccination status. Malaysian Journal
of Medicine and Health Sciences 19(Suppl 16): 16-23.
Plummer, M., Best, N., Cowles, K., Vines, K., Sarkar,
D., Bates, D., Almond, R. & Magnusson, A. 2024. coda: Output analysis
and diagnostics for MCMC (Version 0.19-4.1) [R package]. CRAN.
Pogorzelska, K., Krętowska, A., Krawczuk-Rybak,
M. & Sawicka-Żukowska, M. 2020. Characteristics of platelet indices
and their prognostic significance in selected medical conditions: A systematic
review. Advances in Medical Sciences 65(2): 310-315.
Ramalu, R.R., Kandasamy, R., Raffiq, A., Idris, Z.
& Adnan, J.S. 2022. Risk factors associated with outcome of spontaneous
intracerebral haemorrhage: Hospital Kuala Lumpur experience. Malaysian Journal of Medical Sciences 29(1): 76-90.
Sabah State Government. 2024. People & History [online]. (Accessed on 23 February 2025).
Sabanayagam, C., He, F., Nusinovici, S., Li, J., Lim,
C., Tan, G. & Cheng, C.Y. 2023. Prediction of diabetic kidney disease risk
using machine learning models: A population-based cohort study of Asian adults. eLife 12: 81878.
Schulman, I.H., Chan, K., Der, J.S., Wilkins, K.J.,
Corns, H.L., Sayer, B., Ngo, D.A., Eggers, P., Norton, J., Shah, N., Mendley, S., Parsa, A., Star, R.A. & Kimmel, P.L. 2023. Readmission and mortality after hospitalization
with acute kidney injury. American Journal of Kidney Diseases 82(1): 63-74.e1.
Seo, I.H. & Lee, Y.J. 2022. Usefulness of complete
blood count (CBC) to assess cardiovascular and metabolic diseases in clinical
settings: A comprehensive literature review. Biomedicines 10(11): 2697.
Sheng, K., Zhang, P., Yao, X., Li, J., He, Y. &
Chen, J. 2020. Prognostic machine learning models for first-year mortality in
incident hemodialysis patients: Development and validation study. JMIR
Medical Informatics 8(10): e20578.
Ting, C.Y., Zakariah, H.
& Mohd Yusri, Y.Z. 2022. Probabilistic risk assessment of COVID-19 patients
at COVID-19 assessment centre. International Journal of Technology 13(6): 1193.
Thombare, D., Bhalerao, A., Dixit, P., Joshi, S. &
Dapkekar, P. 2023. Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte
ratio in antenatal women with pre-eclampsia: A case-control study. Cureus 15(6): e40338.
Thölke, P., Mantilla-Ramos, Y.J., Abdelhedi, H.,
Maschke, C., Dehgan, A., Harel, Y., Kemtur, A.,
Berrada, L.M., Sahraoui, M., Young, T., Pépin, A.B.,
El Khantour, C., Landry, M., Pascarella, A., Hadid,
V., Combrisson, E., O’Byrne, J. & Jerbi, K. 2023.
Class imbalance should not throw you off balance: Choosing the right
classifiers and performance metrics for brain decoding with imbalanced data. NeuroImage 277: 120253.
Tsheten, T., Alene, K.A., Restrepo, A.C., Kelly, M., Lau, C., Clements, A.C.A.,
Gray, D.J., Daga, C., Mapalo, V.J., Espino, F.E. & Wangdi, K. 2024. Risk
mapping and socio-ecological drivers of soil-transmitted helminth infections in
the Philippines: A spatial modelling study. The Lancet Regional Health –
Western Pacific 43: 100974.
Uludag, K. & Arikan, T. 2021. Is white blood cell
count associated with mortality in peritoneal dialysis patients? A
retrospective single-center analysis. Cureus 13(11): e19728.
Umeres-Francia, G.E., Rojas-Fernández, M.V., Herrera-Añazco, P. & Benites-Zapata, V.A. 2022.
Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as risk factors
for mortality in Peruvian adults with chronic kidney disease. Renal Replacement
Therapy 8: 30.
van de Schoot, R., Veen, D., Smeets, L., Winter, S.D.
& Depaoli, S. 2020. A tutorial on using the WAMBS checklist to avoid the
misuse of Bayesian statistics. In Small Sample Size Solutions: A Guide for Applied
Researchers and Practitioners, edited by van de Schoot,
R. & Miočević, M. Abingdon: Routledge.
pp. 30-49.
Wang, Z., Nie, J., Song, X., Denson, L.A. & Hyams,
J.S. 2024. A comparison of Bayesian and frequentist approaches to incorporating
clinical and biological information for the prediction
of response to standardized pediatric colitis
therapy. PLoS ONE 19(3): e0295814.
Wang, J., Ma, X., Si, X., Wu, M. & Han, W. 2023.
Mean platelet volume and the association with all-cause mortality and
cardiovascular mortality among incident peritoneal dialysis patients. BMC
Cardiovascular Disorders 23(1): 543.
Wei, L., Mao, S., Liu, X. & Zhu, C. 2024.
Association of systemic inflammation response index with all-cause mortality as
well as cardiovascular mortality in patients with chronic kidney disease. Frontiers
in Cardiovascular Medicine 11: 1363949.
Wolde, H.F., Clements, A.C., Gilmour, B. & Alene,
K.A. 2024. Spatial co-distribution of tuberculosis prevalence and low BCG
vaccination coverage in Ethiopia. Scientific Reports 14(1): 31561.
Wong, R.S. & Ismail, N.A. 2016. An application of
Bayesian approach in modeling risk of death in an
intensive care unit. PLoS ONE 11(3):
e0151949.
Wong, J.E., Yamaguchi, M., Nishi, N., Araki, M. &
Wee, L.H. 2022. Predicting overweight and obesity status among Malaysian
working adults with machine learning or logistic regression: Retrospective comparison study. JMIR
Formative Research 6(12).
World Health Organization. 2024a. The Top 10 Causes
of Death [online]. (Accessed on 21 February 2025).
World Health Organization. 2024b. Data – Malaysia
[online]. (Accessed on 21 February 2025).
Wulandari, E. & Hapsari, R.A.F. 2024. Innovative
hematology analysis using menstrual blood. Medical Devices: Evidence and
Research 17: 285-293.
Yadav, R., Yadav, V., Pokhriyal, S., Zahid, U. &
Gandhi, A. 2023. Retrospective observational study of complete blood count
(CBC) parameters and ICU mortality of COVID-19 disease in Delta variant and
Omicron variant in a community-based hospital in New York City. Cureus 15(2):
e34894.
Yang, K., Yang, N., Sun, W., Dai, L., Jin, J., Wu, J.
& He, Q. 2023. The association between albumin and mortality in patients
with acute kidney injury: A retrospective observational study. BMC
Nephrology 24(1): 332.
Yip, W., Ng, S.H.X., Kaur, P., George, P.P., Guan,
J.H.C., Lee, G., Koh, T.J.K., Tan, W.S. & Hum, A.Y.M. 2024. Risk factors for short-term all-cause mortality
in patients with end-stage renal disease: A scoping review. BMC Nephrology 25(1): 71.
Zhao, X., Karaboyas, A., Gan, L., Hou, F.F., Liang,
X., Chen, X., Chen, Y., Ni, Z., Pecoits-Filho, R.
& Zuo, L. 2024. Platelet count has a U-shaped association with mortality in
hemodialysis patients. Scientific Reports 14(1): 26572.
*Corresponding author; email:
liyana87@ums.edu.my
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