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Statistical medicine is the science that takes help of statistical evidence for managing health and disease.[1] The statistical evidence is generally empirical that arises directly or indirectly from observations and experiments.[2] The validity and reliability of this evidence for medical decisions are generally assessed by appropriate statistical tools that provide confidence in using this evidence for patient management. Health is understood as the dynamic state that keeps balanced homeostasis for proper functioning of the body systems[3] and medicine comprises steps to bring the system back on track when an aberration occurs.[4] It includes the practices and procedures used for prevention, treatment, or relief of the ailments.[5] Medicine becomes statistical when statistical methods are used to understand or explain the clinical evidence and their consequences, and becomes personalized when these methods are used for individual patients. These methods helps in enhancing the objectivity in clinical decisions and generally consider opposite. This is generally considered opposite to diagnosis and treatment decisions based on clinical acumen of the physicians rather than empirical evidence.[6]

History

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The first use of the term “statistical medicine” can be traced back to 1823 when an army officer referred to the calculations of mortality in troops by different diseases.[7] Kish[8] refers the term statistical medicine to analysis and collection of data related to medicine. This continued to be the dominant view, and the science concerned with this was referred to medical statistics or biostatistics until it emerged that statistical medicine is a medical specialty practiced at personalized level,[9] whereas medical statistics is a subdiscipline of statistics.

Personalized statistical medicine

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Personalized statistical medicine is reaching to clinical decisions regarding diagnosis, treatment, and prognosis in individual cases by using statistical tools. Prominent among these tools are scoring systems, indexes, scales, models, decision trees, and artificial intelligence/ machine learning processes.[10] These tools help in reaching to a more objective decision. The following are some examples of clinical decisions based on different statistical tools and illustrate personalized statistical medicine.

Scoring systems

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A scoring system assigns scores to various signs-symptoms and other clinical features of the patients depending on their severity with score = 0 for the absence of the feature. The sum of these scores is used to reach to a clinical decision regarding diagnosis and prognosis, and to assess their severity that guides the treatment regimen. Sometimes a threshold is used for assessing the presence or absence of a health condition. A scoring system is available for early diagnosis of malignant pleural infusion[11] and Alvarado scoring is used for diagnosis of acute appendicitis.[12] APACHE-II score is used for assessing prognosis of critically ill patients[13] and a scoring system is used for forecasting short term survival of prostate cancer patients.[14]

Indexes

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Indexes are combination of two or more indicators such as body mass index is of height and weight. Such indexes provide more comprehensive picture than individual indicators. Among various indexes used for diagnosis are Copenhagen index for malignant adnexal tumours[15] and hepatorenal index for steatosis in fatty liver disease.[16] Visceral adiposity index can be used for predicting short-term mortality of patients with acute ischemic stroke[17] and triglyceride-glucose index was investigated for predicting short-term functional outcome in patients with ischemic stroke.[18]

Scales

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Scales are similar to scores but generally less complex. Higher the reading on the scale, more severe is the disease. Scales also are generally based on signs-symptoms and other clinical conditions. Wender Utah Rating Scale has association with clinical psychiatric diagnosis in adulthood[19] and there is a rating scale for diagnosis and assessment of catatonia.[20] Glasgow Coma Scale is used to predict the severity of emergency cases[21] and Clinical Frailty Scale has been found to predict short-term mortality in emergency cases.[22]

Models

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Statistical models are like equations that combine various clinical features of a patients with the weight they deserve according to their importance in determining an outcome. Among many statistical models for clinical applications, there is one that leads from pruritus to cholestasis to predict diagnosis prior to bile acid determination[23] and there is another for differential diagnosis of bacterial and viral meningitis in childhood.[24] A mortality risk prediction model has been proposed for children with acute myocarditis.[25]

Decision trees

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A decision tree describes a stepwise procedure to gradually reach to focused conclusion after considering the probabilities of various possibilities at each stage. A decision tree-based approach is available for pressure ulcer risk assessment in immobilized patients[26] and there is a decision tree algorithm for breast cancer diagnosis.[27] A decision-tree algorithm was developed for prediction of CoViD mortality based on biomarkers such as ferritin and D-dimer,[28] and there is one to study effect of air pollution on under-five mortality.[29]

Artificial intelligence (AI) and machine learning (ML)

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AI and ML have made inroads to medicine as they too have been found to greatly enhance the objectivity in clinical decisions with strong interface with computers, AI emulates human intelligence by perceiving, synthesizing, and inferring data through computer-based machines. ML is building methods that learn by leveraging data to improve performance. Both require data and basically based on statistical methods. AI has been proposed for the diagnosis of Helicobacter pylori[30] and for risk profiling for prevention and treatment of chronic wounds.[31] ML methods have been described for discoveringbiomarkers significant for survival.[32] ML approaches have also been found useful in identification of paediatric epilepsy[33] and in prediction of complete remission and survival in acute myeloid leukaemia.[34]

Others

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Several global statistical concepts are also used for personalized medicine. Among them are probability used in diagnosis and prognosis, relative risk and odds ratio used for risk assessment, sensitivity-specificity-predictivities and C-statistic used for assessing the performance of medical tests and mean ± 2 SD range used as reference intervals for many medical measurements. They have direct applications to clinical decisions at individual level, and important tools for statistical medicine.[citation needed]

Scope

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Because of widespread use of statistical tools to achieve objectivity in diagnosis, treatment, and prognosis decisions, statistical medicine is emerging as a distinct medical specialty[9] . This is similar to laboratory medicine where clinical decisions regarding health and disease are taken with the help of laboratory results. The role of statistical medicine too is limited to providing help to clinicians to take more objective decisions – the decision remains the role responsibility of the clinicians as with many other helping tools.[citation needed]

References

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  1. ^ Datta, Sujay; Xia, Xiao-Qin; Bhattacharjee, Samsiddhi; Jia, Zhenyu (2014). "Advances in Statistical Medicine". Computational and Mathematical Methods in Medicine. 2014: 1–2. doi:10.1155/2014/316153. PMC 4241697. PMID 25435899.
  2. ^ "Empirical evidence | Definition, Examples, Evidentialism, Foundationalism, & Facts | Britannica". www.britannica.com. 2024-09-20. Retrieved 2024-10-14.
  3. ^ "Definition of homeostasis". www.cancer.gov. 2011-02-02. Retrieved 2024-10-14.
  4. ^ Indrayan A, Malhotra RK. Medical Biostatistics. Fourth Edition. CRC Press, 2018, p1.
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  7. ^ An Army Medical Officer (April 1823). "Remarks on the Diseases of Tropical Climates; with Some Calculations Relative to Statistical Medicine". The London Medical and Physical Journal. 49 (290): 289–297. PMC 5632405. PMID 30494472.
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  11. ^ Wu, Aihua; Liang, Zhigang; Yuan, Songbo; Wang, Shanshan; Peng, Weidong; Mo, Yijun; Yang, Jing; Liu, Yanqing (7 December 2021). "Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram". Frontiers in Oncology. 11: 775079. doi:10.3389/fonc.2021.775079. PMC 8688822. PMID 34950585.
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  15. ^ Yue, Xiang; Yue, Zhenni; Wang, Ying; Dong, Zhenjin; Yang, Hua; Yue, Sifeng (February 2023). "Value of the Copenhagen index in the diagnosis of malignant adnexal tumors: A meta-analysis". International Journal of Gynecology & Obstetrics. 160 (2): 506–515. doi:10.1002/ijgo.14310. PMID 35696160.
  16. ^ Kjaergaard, Maria; Lindvig, Katrine Prier; Hansen, Camilla Dalby; Detlefsen, Sönke; Krag, Aleksander; Thiele, Maja (February 2023). "Hepatorenal Index by B-Mode Ratio Versus Imaging and Fatty Liver Index to Diagnose Steatosis in Alcohol-Related and Nonalcoholic Fatty Liver Disease". Journal of Ultrasound in Medicine. 42 (2): 487–496. doi:10.1002/jum.15991. PMC 10084348. PMID 35475550.
  17. ^ Chang, Yuhong; Zhang, Lulu; Li, Yidan; Wang, Dapeng; Fang, Qi; Tang, Xiang (10 February 2023). "Derivation and Validation of a New Visceral Adiposity Index for Predicting Short-Term Mortality of Patients with Acute Ischemic Stroke in a Chinese Population". Brain Sciences. 13 (2): 297. doi:10.3390/brainsci13020297. PMC 9954352. PMID 36831840.
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  19. ^ Hanley, Cliodhna; Saleem, Faisal; Graffeo, Ignazio; McCarthy, Geraldine; Gavin, Blánaid; McNicholas, Fiona; Adamis, Dimitrios (February 2022). "Association of Wender Utah Rating Scale (WURS)-61 items with clinical psychiatric diagnosis in adulthood". Irish Journal of Medical Science (1971 -). 191 (1): 327–335. doi:10.1007/s11845-021-02574-7. PMID 33665779.
  20. ^ Hirjak, Dusan; Brandt, Geva A.; Fritze, Stefan; Kubera, Katharina M.; Northoff, Georg; Wolf, Robert Christian (January 2024). "Distribution and frequency of clinical criteria and rating scales for diagnosis and assessment of catatonia in different study types". Schizophrenia Research. 263: 93–98. doi:10.1016/j.schres.2022.12.019. PMID 36610862.
  21. ^ Yuksen, Chaiyaporn; Angkoontassaneeyarat, Chuenruthai; Thananupappaisal, Sorawat; Laksanamapune, Thanakorn; Phontabtim, Malivan; Namsanor, Pamorn (March 2023). "Accuracy of Trauma on Scene Triage Screening Tool (Shock Index, Reverse Shock Index Glasgow Coma Scale and National Early Warning Score) to Predict the Severity of Emergency Department Triage: A Retrospective Cross-Sectional Study". Open Access Emergency Medicine. 15: 79–91. doi:10.2147/OAEM.S403545. PMC 10039710. PMID 36974278.
  22. ^ Lee, Ji Hwan; Park, Yoo Seok; Kim, Min Joung; Shin, Hye Jung; Roh, Yun Ho; Kim, Ji Hoon; Chung, Hyun Soo; Park, Incheol; Chung, Sung Phil (November 2022). "Clinical Frailty Scale as a predictor of short-term mortality: A systematic review and meta-analysis of studies on diagnostic test accuracy". Academic Emergency Medicine. 29 (11): 1347–1356. doi:10.1111/acem.14493. PMID 35349205.
  23. ^ Ramirez Zamudio, Andres F.; Monrose, Erica; Pan, Stephanie; Ferrara, Lauren (July 2021). "From Pruritus to Cholestasis: Building a Statistical Model and Online Application to Predict a Diagnosis Prior to Bile Acid Determination". American Journal of Perinatology. 38 (9): 889–996. doi:10.1055/s-0041-1729160. PMID 33934325.
  24. ^ Çelik, Neşat; Tanir, Gönül; Aydemir, Cumhur; Tuygun, Nilden; Zorlu, Pelin (2007). "Çocukluk Çağı Akut Menenjit Olgularında Bakteriyel ve Viral Menenjit Ayırıcı Tanısı: İstatistiksel Bir Model" [Differential Diagnosis of Bacterial and Viral Menengitis in Childhood Acute Meningitis: A Statistical Model] (PDF). Mikrobiyoloji Bülteni (in Turkish). 41 (1): 63–69. PMID 17427553.
  25. ^ Zhuang, Shi-Xin; Shi, Peng; Gao, Han; Zhuang, Quan-Nan; Huang, Guo-Ying (2023). "Clinical characteristics and mortality risk prediction model in children with acute myocarditis". World Journal of Pediatrics. 19 (2): 180–188. doi:10.1007/s12519-022-00637-y. PMC 9928813. PMID 36378481.
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  27. ^ Tian, Jian-xue; Zhang, Jue (2022). "Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor". Mathematical Biosciences and Engineering. 19 (3): 2193–2205. doi:10.3934/mbe.2022102. PMID 35240781.
  28. ^ Huyut, Mehmet Tahir; Huyut, Zübeyir (March 2023). "Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees". Heliyon. 9 (3): e14015. Bibcode:2023Heliy...914015H. doi:10.1016/j.heliyon.2023.e14015. PMC 9985543. PMID 36919085.
  29. ^ Tileubai, Akhit; Tsend, Javzmaa; Oyunbileg, Bat-Enkh; Luvsantseren, Purevdolgor; Luvsan-Ish, Ajnai; Chilhaasuren, Baasandorj; Puntsagdash, Jargalbat; Chuluunbaatar, Galbadrakh; Tsagaan, Baatarkhuu (March 2023). "Study of decision tree algorithms: effects of air pollution on under five mortality in Ulaanbaatar". BMJ Health & Care Informatics. 30 (1): e100678. doi:10.1136/bmjhci-2022-100678. PMC 9980318. PMID 36858462.
  30. ^ Liscia, Daniel S.; D’Andrea, Mariangela; Biletta, Elena; Bellis, Donata; Demo, Kejsi; Ferrero, Franco; Petti, Alberto; Butinar, Roberto; D’Andrea, Enzo; Davini, Giuditta (August 2022). "Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies". Pathologica. 114 (4): 295–303. doi:10.32074/1591-951X-751. PMC 9624136. PMID 36136897.
  31. ^ Cross, Karen; Harding, Keith (21 September 2022). "Risk profiling in the prevention and treatment of chronic wounds using artificial intelligence". International Wound Journal. 19 (6): 1283–1285. doi:10.1111/iwj.13952. PMC 9493230. PMID 36131590.
  32. ^ Yao, Sijie; Wang, Xuefeng (2023). "Statistical and Machine Learning Methods for Discovering Prognostic Biomarkers for Survival Outcomes". Statistical Genomics. Methods in Molecular Biology. Vol. 2629. pp. 11–21. doi:10.1007/978-1-0716-2986-4_2. ISBN 978-1-0716-2985-7. PMID 36929071.
  33. ^ Ahmed, Mohammed Imran Basheer; Alotaibi, Shamsah; Dash, Sujata; Nabil, Majed; AlTurki, Abdullah Omar (2022). "A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy". Sn Computer Science. 3 (6): 437. doi:10.1007/s42979-022-01358-9. PMC 9364307. PMID 35965953.
  34. ^ Eckardt, Jan-Niklas; Röllig, Christoph; Metzeler, Klaus; Kramer, Michael; Stasik, Sebastian; Georgi, Julia-Annabell; Heisig, Peter; Spiekermann, Karsten; Krug, Utz; Braess, Jan; Görlich, Dennis; Sauerland, Cristina M.; Woermann, Bernhard; Herold, Tobias; Berdel, Wolfgang E.; Hiddemann, Wolfgang; Kroschinsky, Frank; Schetelig, Johannes; Platzbecker, Uwe; Müller-Tidow, Carsten; Sauer, Tim; Serve, Hubert; Baldus, Claudia; Schäfer-Eckart, Kerstin; Kaufmann, Martin; Krause, Stefan; Hänel, Mathias; Schliemann, Christoph; Hanoun, Maher; Thiede, Christian; Bornhäuser, Martin; Wendt, Karsten; Middeke, Jan Moritz (16 June 2022). "Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning". Haematologica. 108 (3): 690–704. doi:10.3324/haematol.2021.280027. PMC 9973482. PMID 35708137.
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