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Statistical medicine

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Statistical medicine is the science that takes the 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 the 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 the prevention, treatment, or relief of ailments [5]. Medicine becomes statistical when statistical methods are used to understand or explain the clinical evidence and its consequences. These methods help in enhancing objectivity in clinical decisions. This is generally considered the opposite of diagnosis and treatment decisions based on the clinical acumen of the physicians rather than empirical evidence [6].

History

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 to the term statistical medicine as the analysis and collection of data related to medicine. This continued to be the dominant view, and the science concerned with this was referred to as medical statistics or biostatistics until it emerged that statistical medicine is a medical specialty practiced at a personalized level [9], whereas medical statistics is a sub discipline of statistics.

Personalized statistical medicine

Personalized statistical medicine is reaching 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

A scoring system assigns scores to various signs-symptoms and other clinical features of the patients depending on their severity with a score = 0 for the absence of the feature. The sum of these scores is used to reach 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 the prognosis of critically ill patients [13] and a scoring system is used for forecasting the short-term survival of prostate cancer patients [14].

Indexes

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

Scales

Scales are similar to scores but generally less complex. The higher the reading on the scale, the more severe the disease. Scales also are generally based on signs-symptoms and other clinical conditions. Wender Utah Rating Scale has an association with a 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

Statistical models are like equations that combine various clinical features of a patient 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

A decision tree describes a stepwise procedure to gradually reach to a 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 the prediction of CoViD mortality based on biomarkers such as ferritin and D-dimer [28], and there is one to study the effect of air pollution on under-five mortality [29].

Artificial intelligence (AI) and machine learning (ML)

AI and ML have made inroads into medicine as they too have been found to greatly enhance the objectivity in clinical decisions with a 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 the prevention and treatment of chronic wounds [31]. ML methods have been described for discovering biomarkers significant for survival [32]. ML approaches have also been found useful in the identification of pediatric epilepsy [33] and in prediction of complete remission and survival in acute myeloid leukemia [34].

Others

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 the individual level and are important tools for statistical medicine.

Scope

Because of the widespread use of statistical tools to achieve objectivity in diagnosis, treatment, and prognosis decisions, statistical medicine is emerging as a distinct medical specialty [35]. 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 sole responsibility of the clinicians as with many other helping tools.

References

  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. ISSN 1748-670X.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  2. ^ Encyclopedia Britannica, "empirical evidence". 3 Feb. 2023, https://www.britannica.com/topic/empirical-evidence. Accessed 1 May 2023.
  3. ^ National Cancer Institute. NCI Dictionary of Cancer Terms. “Homeostasis”. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/homeostasis
  4. ^ Indrayan, Abhaya; Malhotra, Rajeev Kumar. Medical biostatistics. Chapman & Hall - CRC biostatistics series (4th ed.). Boca Raton: CRC press. ISBN 978-1-4987-9953-9.
  5. ^ National Cancer Institute. NCI Dictionary of Cancer Terms. “Medicine”. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/medicine
  6. ^ Murray, A. (2000-04-01). "Part-time Physicians: Physician Workload and Patient-Based Assessments of Primary Care Performance". Archives of Family Medicine. 9 (4): 327–332. doi:10.1001/archfami.9.4.327. ISSN 1063-3987.
  7. ^ "Remarks on the Diseases of Tropical Climates; with Some Calculations Relative to Statistical Medicine". The London Medical and Physical Journal. 49 (290). An Army Medical Officer: 289–297. 1823-02-14. doi:10.1056/nejm188703031160910. ISSN 0267-0259. PMC 5632405. PMID 30494472.{{cite journal}}: CS1 maint: others (link)
  8. ^ LESLIE, KISH (29 Jul 1994). "Statistical medicine". Science. 265 (5172).
  9. ^ Indrayan, A (2017). "Statistical medicine: An emerging medical specialty". Journal of Postgraduate Medicine. 63 (4): 252. doi:10.4103/jpgm.JPGM_189_17. ISSN 0022-3859.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  10. ^ Indrayan, Abhaya (January 2023). "Personalized statistical medicine". Indian Journal of Medical Research. 157 (1): 104–108. doi:10.4103/ijmr.ijmr_1510_22. ISSN 0971-5916.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  11. ^ Wu, Aihua; Liang, Zhigang; Yuan, Songbo; Wang, Shanshan; Peng, Weidong; Mo, Yijun; Yang, Jing; Liu, Yanqing (2021-12-07). "Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram". Frontiers in Oncology. 11. doi:10.3389/fonc.2021.775079. ISSN 2234-943X.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  12. ^ El Hattabi, Khalid; Bouali, Mounir; El Berni, Yassine; Bensardi, Fatimazahra; El Bakouri, Abdelilah; Moufakkir, Aziz; Riad, Noha; Fadil, Abdelaziz (May 2022). "Value of Alvarado scoring system in diagnosis of acute appendicitis". Annals of Medicine & Surgery. 77. doi:10.1016/j.amsu.2022.103642. ISSN 2049-0801.
  13. ^ Cao, Yongmei; Yao, Sijia; Shang, Jiawei; Ping, Feng; Tan, Qin; Tian, Zijun; Huang, Weifeng; Li, Yingchuan (2022-12-09). "The combination of lactate level, lactate clearance and APACHE II score better predicts short-term outcomes in critically Ill patients: a retrospective cohort study". BMC Anesthesiology. 22 (1). doi:10.1186/s12871-022-01878-0. ISSN 1471-2253.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  14. ^ Zhou, Yuan; Lin, Changming; Zhu, Lian; Zhang, Rentao; Cheng, Lei; Chang, Yuanyuan (February 2023). "Nomograms and scoring system for forecasting overall and cancer‐specific survival of patients with prostate cancer". Cancer Medicine. 12 (3): 2600–2613. doi:10.1002/cam4.5137. ISSN 2045-7634.
  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. ISSN 0020-7292.
  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. ISSN 0278-4297.
  17. ^ Chang, Yuhong; Zhang, Lulu; Li, Yidan; Wang, Dapeng; Fang, Qi; Tang, Xiang (2023-02-10). "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. ISSN 2076-3425.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  18. ^ Miao, Mengyuan; Bi, Yucong; Hao, Lijun; Bao, Anran; Sun, Yaming; Du, Huaping; Song, Liyan; You, Shoujiang; Zhong, Chongke (February 2023). "Triglyceride-glucose index and short-term functional outcome and in-hospital mortality in patients with ischemic stroke". Nutrition, Metabolism and Cardiovascular Diseases. 33 (2): 399–407. doi:10.1016/j.numecd.2022.11.004.
  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. ISSN 0021-1265.
  20. ^ Hirjak, Dusan; Brandt, Geva A.; Fritze, Stefan; Kubera, Katharina M.; Northoff, Georg; Wolf, Robert Christian (January 2023). "Distribution and frequency of clinical criteria and rating scales for diagnosis and assessment of catatonia in different study types". Schizophrenia Research. doi:10.1016/j.schres.2022.12.019.
  21. ^ C, Yuksen (2023-03-21). "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 Emerg Med. 29 (11): 79–91. doi:10.2147/OAEM.S403545. PMC 10039710. PMID 36974278.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  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. ISSN 1069-6563.
  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 (09): 889–996. doi:10.1055/s-0041-1729160. ISSN 0735-1631.
  24. ^ Neşat, Celik; Gönül, Tanir; Cumhur, Aydemir; Nilden, Tuygun; Pelin, Zorlu. "Cocukluk Caği akut menenjit olgularinda bakteriyel ve viral menenjitin ayirici tanisi: istatistiksel bir model [Differential diagnosis of bacterial and viral meningitis in childhood acute meningitis: a statistical model]". Mikrobiyoloji bülteni 2007. 41 (1): 63–69. ISSN 0374-9096. PMID 17427553.
  25. ^ Zhuang, Shi-Xin; Shi, Peng; Gao, Han; Zhuang, Quan-Nan; Huang, Guo-Ying (February 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. ISSN 1708-8569.
  26. ^ Vera-Salmerón, Eugenio; Domínguez-Nogueira, Carmen; Romero-Béjar, José L.; Sáez, José A.; Mota-Romero, Emilio (2022-09-06). "Decision-Tree-Based Approach for Pressure Ulcer Risk Assessment in Immobilized Patients". International Journal of Environmental Research and Public Health. 19 (18): 11161. doi:10.3390/ijerph191811161. ISSN 1660-4601.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  27. ^ Tian, Jian-xue; Zhang, Jue; 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. ISSN 1551-0018.
  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. doi:10.1016/j.heliyon.2023.e14015.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  29. ^ Tileubai A, Tsend J, Oyunbileg BE, Luvsantseren P, Luvsan-Ish A, Chilhaasuren B, Puntsagdash J, Chuluunbaatar G, Tsagaan B. Study of decision tree algorithms: effects of air pollution on under five mortality in Ulaanbaatar. BMJ Health Care Inform. 2023 Mar;30(1):e100678. doi: 10.1136/bmjhci-2022-100678. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980318/
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  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. (2022-06-16). "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. ISSN 1592-8721.
  35. ^ Indrayan, A (2017). "Statistical medicine: An emerging medical specialty". Journal of Postgraduate Medicine. 63 (4): 252. doi:10.4103/jpgm.JPGM_189_17. ISSN 0022-3859.{{cite journal}}: CS1 maint: unflagged free DOI (link)
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