Validation of Prediction Algorithm for Risk Estimation of Intracranial Aneurysm Development using Real-world Data

doi:10.21203/rs.3.rs-2822326/v1

Download PDF

Article

Validation of Prediction Algorithm for Risk Estimation of Intracranial Aneurysm Development using Real-world Data

https://doi.org/10.21203/rs.3.rs-2822326/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 05 Sep, 2023

Read the published version in Scientific Reports →

You are reading this latest preprint version

Background

Intracranial aneurysm (IA) is difficult to detect, and most patients remain undiagnosed, as screening tests have potential risks and high costs. Thus, it is important to develop risk assessment system for efficient and safe screening strategy. We designed a clinical validation study to test the efficacy and validity of an artificial intelligence (AI) based risk prediction model for intracranial aneurysm in an actual clinical setting.

Materials and Methods

The study population comprised individuals who visited the Chonnam National University Hwasun Hospital Health Promotion Center in Korea for voluntary medical checkups between 2007 and 2019. All participants had no history of cerebrovascular disease and underwent brain CTA for screening purpose. Presence of IA was evaluated by two specialized radiologists. The risk score was calculated using the previously developed AI model, and 0 point represents the lowest risk and 100 point represents the highest risk. To compare the prevalence according to the risk, age-sex standardization using national database was performed.

Result

A study collected data from 5,942 health examinations, including brain CTA data, with participants ranging from 20 to 87 years old and a mean age of 52 years. The age-sex standardized prevalence of IA was 3.20%. The prevalence in each risk group was 0.18% (lowest risk, 0–19), 2.12% (lower risk, 20–39), 2.37% (mid-risk, 40–59), 4.00% (higher risk, 60–79), and 6.44% (highest risk, 80–100). The odds ratio between the lowest and highest risk groups was 38.50. The adjusted proportions of IA patients in the higher and highest risk groups were 26.7% and 44.5%, respectively. The median risk scores among IA patients and normal participants were 74 and 54, respectively. The optimal cut-off risk score was 60.5 with an area under the curve of 0.70.

Conclusion

We showed the clinical efficacy and validity of a prediction algorithm for risk estimation of IA using real-world data.

artificial intelligence

intracranial aneurysm

risk

prediction

Intracranial aneurysm (IA) is among the most dangerous diseases owing its clinical course related to subarachnoid hemorrhage (SAH), which is led by rupture of it.¹ The case fatality of SAH is known to be high as 35%, and only a third of survivors can resume their premorbid work.² To prevent SAH, adequate treatment should be provided for the patients with rupture prone unruptured IA. However, detecting unruptured IA mainly relies on the incidental findings because there are few symptoms related to IA. Besides, the guidelines for IA screening are covering very narrow extent of populations such as those with familial history (≥ two family members with IA or SAH) or genetic diseases (autosomal dominant polycystic kidney disease, microcephalic osteodysplastic primordial dwarfism).^3,4 Moreover, diagnostic modalities, such as computed tomography angiography (CTA) or magnetic resonance angiography (MRA), cannot be easily performed due to the potential risk of side effects such as radiation and contrast agents or very high cost. Thus, the prevalence and incidence of IA tends to be estimated very low. Indeed, only 0.44% of populations during a 8.8 million person-year observation were diagnosed as having unruptured IA or SAH.¹ Contrarily, the prevalences among participants who underwent MRA were reported as 2.8–7.0%.^5–7 To put it all together, most IA patients remain undiagnosed because they have never undergone adequate examinations.

As described above, screening tests, such as CTA or MRA, cannot be recommended for all populations due to potential risks (medical supply capacity, radiation exposure, contrast agent side effects, claustrophobia) and high cost. Thus, we need to identify the individual risk for IA and find high-risk populations requiring screening tests. For this, we had investigated the risk factors of IA and proposed an artificial intelligence-based prediction model, which had been trained by using national health examination data combined with medical utilization data including diagnoses.^1,4 In this study, we designed a clinical validation study with the algorithm in the actual clinical site to prove its clinical efficacy and validity.

Study Populations

The study participants were those who visited the Chonnam National University Hwasun Hospital (CNUHH) Health Promotion Center of the Republic of Korea for voluntary medical checkups between January 2007 and December 2019. We conducted a retrospective review of the participants’ medical records. The participants’ data were anonymized and stored in the CNUHH Clinical Data Warehouse. The data were used for research purposes in accordance with the Personal Information Protection Act of the Republic of Korea. Each participant provided electronic informed consent regarding the collection and use of personal information before the checkup was conducted. All participants in this study have no history of cerebrovascular disease and are currently asymptomatic, as they had a consultation in the treatment department instead of a health checkup if they had any special symptoms. The study protocol was approved by the Institutional Review Board (IRB) of CNUHH (IRB number CNUHH-2022-207) and all of research processes were performed in accordance with relevant guidelines and regulations.

Data collection

The participants were interviewed by trained physicians using a structured questionnaire with items on smoking and familial history of diseases (stroke, diabetes mellitus, heart disease, hypertension, and so on). Smokers were defined as those who smoked ≥ 100 cigarettes in their lifetime. A person who had quit smoking for ≥ 1 year was defined as a former smoker. Height and weight were measured in the upright position without shoes and socks. Waist circumference was measured at the mid-point between the 12th rib and anterior superior iliac spine. The body mass index (BMI) was calculated by dividing the weight (kg) by the squared height (m²). Blood pressure was measured in the left upper arm using a regularly calibrated electronic sphygmomanometer, after the participants has rested for > 10 minutes. Blood tests were performed after fasting for > 12 hours. Brain CTA was performed using a 128-slice dual source CT scanner (SOMATOM Definition Flash, Siemens, Germany) after 2011. Prior to 2011, a 64-slice CT scanner (LightSpeed VCT, GE Healthcare, Milwaukee, WI, USA) was used. After imaging, two experienced radiology specialists cross-examined the presence of IA.

Inference with trained algorithm

Twenty-one variables (age, sex, BMI, waist circumference, systolic and diastolic blood pressures, fasting glucose, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglyceride, hemoglobin, creatinine, gamma-glutamyl transferase, aspartate aminotransferase, alanine aminotransferase, smoking status, and familial histories of stroke, hypertension, heart disease, and diabetes mellitus) were substituted to the inference algorithm. The result values returned by the inference algorithm were risk scores that were presented as integers from 0 to 100, with higher scores indicating a higher risk).

Statistical Analyses

We used Python (version 3.11; Python software foundation; https://www.python.org) and R (version 4.2.0; the R Foundation for Statistical Computing; https://www.r-project.org) for inference and statistical analyses.

Age-sex standardization was performed using a reference dataset that had been prepared as a test dataset in the previous research.⁴ It comprises 128,181 individuals with general health examination data who were labeled for the presence of IA. As it was curated by stratified random selection among the whole national population, it can represent the demographic characteristics of the entire population who participated in the healthcare screening program.⁸ Age was grouped into 10-year-old units for standardization. Using age group and sex, the propensity scores were calculated for each individual. Based on the propensity scores, the weights were determined and applied to produce age-sex standardized prevalence of intracranial aneurysm. The risk score was defined as a ranking score calculated from probability, where 0 is the lowest and 100 is the highest.

Generally, p-value < 0.05 was accepted for statistical significance. Continuous variables were presented as mean ± standard deviation and analyzed by using Student t-test. As the risk score was ranked, it was presented as median (interquartile range, IQR) and analyzed by using Mann-Whitney U test. Categorical variables were presented as proportion (%) and analyzed by using chi square test or Fisher’s exact test according to the expected counts. To evaluate the linear trend of the risk score, risk scores were divided into the following quintile groups as defined in previous work⁴: 0–19, lowest risk; 20–39, lower risk; 40–59; mid risk; 60–79, higher risk; and 80–100, highest risk. The extended Mantel Haenszel chi square test was used to calculate statistical significance of the scoring system.

Through a data collection process, a total of 5,942 health examination data, including brain CTA data, were collected. The participants’ age range of was 20 to 87 years, with a mean of 52.15 ± 8.82 years. The proportion of female participants was 33.6%. Among them, 237 were diagnosed with IA. Thus, the crude prevalence of IA was 4.00% (95% confidence interval [CI], 3.52–4.53). The other variables of the enrolled participants are summarized in Table 1.

Table 1

Summarization of data.
Factor		NHIS	CNUHH	p.value	CNUHH	p.value
Factor		n = 128,181	n = 5,942	p.value	age/sex adjusted	p.value
Age group	20≤, < 30	14.3%	0.3%	< 0.001	14.3%	1.000
	30≤, < 40	17.3%	6.5%		17.3%
	40≤, < 50	28.5%	32.8%		28.5%
	50≤, < 60	21.9%	41%		21.9%
	60≤, < 70	11.8%	15.9%		11.8%
	70≤, < 80	5.4%	3.2%		5.4%
	80≤	0.9%	0.2%		0.9%
Sex (female)		51.2%	33.6%	< 0.001	51.2%	1.000
BMI (kg/m²)		23.55 ± 3.29	24.48 ± 2.86	< 0.001	24.10 ± 3.57	< 0.001
Waist circumference (cm)		79.41 ± 9.31	83.20 ± 8.37	< 0.001	81.45 ± 10.16	< 0.001
Systolic BP (mmHg)		121.18 ± 14.68	121.06 ± 15.80	0.543	118.14 ± 16.46	< 0.001
Diastolic BP (mmHg)		75.56 ± 9.93	71.67 ± 10.57	< 0.001	69.75 ± 10.69	< 0.001
Glucose (mg/dl)		95.07 ± 16.12	103.34 ± 25.53	< 0.001	99.61 ± 23.55	< 0.001
Total cholesterol (mg/dl)		193.67 ± 35.88	194.67 ± 37.17	0.069	192.81 ± 36.47	< 0.001
LDL cholesterol (mg/dl)		113.24 ± 32.85	122.81 ± 34.17	< 0.001	120.99 ± 33.84	< 0.001
HDL cholesterol (mg/dl)		55.85 ± 13.81	49.29 ± 11.77	< 0.001	51.00 ± 12.02	< 0.001
Triglyceride (mg/dl)		122.28 ± 72.61	128.42 ± 93.19	< 0.001	123.17 ± 94.31	0.013
Hemoglobin (g/dl)		13.86 ± 1.61	14.67 ± 1.51	< 0.001	14.35 ± 1.52	< 0.001
Creatinine (mg/dl)		0.89 ± 0.22	0.95 ± 0.18	< 0.001	0.90 ± 0.18	< 0.001
AST (IU/L)		23.74 ± 8.72	28.30 ± 16.07	< 0.001	28.65 ± 21.02	< 0.001
ALT (IU/L)		22.97 ± 14.11	28.98 ± 19.39	< 0.001	30.16 ± 28.11	< 0.001
GGT (IU/L)		31.31 ± 28.85	44.76 ± 49.78	< 0.001	37.25 ± 44.07	< 0.001
Family History	Stroke	5.5%	8.3%	< 0.001	7.1%	< 0.001
	DM	9.2%	16.1%	< 0.001	15.3%	< 0.001
	Heart disease	3.4%	4.6%	< 0.001	4.6%	< 0.001
	Hypertension	11.7%	24.4%	< 0.001	24.5%	< 0.001
Smoking	Never	62.7%	50.4%	< 0.001	65.3%	< 0.001
	Former	12.7%	26.5%		15.9%
	Current	24.6%	23.1%		18.8%

Continuous variables are presented as mean ± standard deviation. Categorical variables are represented as percentages. NHIS = National Health Insurance Service; CNUHH = Chonnam National University Hwasun Hospital; BMI = body mass index; BP = blood pressure; LDL = low-density lipoprotein; HDL = high-density lipoprotein; AST = aspartate aminotransferase; ALT = alanine transaminase; GGT = gamma-glutamyl transferase; DM = diabetes mellitus.

Among 78 participants who were classified as the lowest risk group, one participant was diagnosed with IA (crude prevalence, 1.28%, 95% CI, 0.23–6.91). The crude prevalence (95% CI) of the lower, mid, higher, and highest risk groups was 2.03% (1.14–3.59), 2.56% (1.89–3.47), 4.01% (3.27–4.91), and 6.23% (5.13–7.54), respectively. The odds ratios of each group compared with the lowest risk group were 1.59, 2.02, 3.22, and 5.11, respectively (Fig. 1 and Table 2). Among all IA cases, 40.5% (96 out of 237) and 37.6% (89 out of 237) occurred in the highest and higher risk groups, respectively. The proportion of IA patients in the higher and highest risk groups reached 78.1%.

Table 2

Incidences, prevalences and odds ratios by predicted risk groups.
Dataset	Predicted Risk Group	No. of Subjects	No. of IA Diagnosed	Incidence (95% CI)	Prevalence (95% CI)	Odds Ratio (95% CI)
NHIS	Lowest	25636	3	0.01 (0.00–0.03)		ref
	Lower	25636	17	0.07 (0.04–0.11)		5.67 (1.66–19.35)
	Mid	25636	40	0.16 (0.12–0.21)		13.35 (3.97–53.96)
	Higher	25636	73	0.29 (0.23–0.36)		24.40 (7.69–77.43)
	Highest	25637	154	0.60 (0.52–0.71)		51.64 (16.47–161.9)
	Sum	128181	287	0.22 (0.20–0.25)
CNUHH	Lowest	78	1		1.28 (0.23–6.91)	ref
	Lower	543	11		2.03 (1.14–3.59)	1.59 (0.20–12.50)
	Mid	1562	40		2.56 (1.89–3.47)	2.02 (0.28–14.92)
	Higher	2217	89		4.01 (3.27–4.91)	3.22 (0.44–23.42)
	Highest	1542	96		6.23 (5.13–7.54)	5.11 (0.70–37.15)
	Sum	5942	237		4.00 (3.52–4.53)
CNUHH adjusted	Lowest	1008.5	1.8		0.18 (0.05–0.69)	ref
	Lower	1056.0	22.4		2.12 (1.41–3.18)	12.12 (2.65–55.46)
	Mid	1293.8	30.7		2.37 (1.67–3.35)	13.59 (3.02–61.25)
	Higher	1269.9	50.8		4.00 (3.05–5.22)	23.30 (5.26–103.3)
	Highest	1313.7	84.6		6.44 (5.24–7.90)	38.50 (8.78–169.0)
	Sum	5942.0	190.2		3.20 (2.78–3.68)
CI = confidence interval; NHIS = National Health Insurance Service dataset; CNUHH = Chonnam National University Hwasun Hospital; ref = reference

As the distributions of age and sex were significantly different from the national population structure, age-sex standardization was performed as described above. As a result, the distributions of age and sex were adjusted to fit the reference dataset (National Health Insurance Service [NHIS]); the age-sex distribution of each dataset is summarized in Table 1 and Fig. 2. The age-sex standardized prevalence of IA was 3.20% (95% CI, 2.78–3.68) among the Chonnam National University Hwasun Hospital (CNUHH) dataset. Applying each weight for age-sex standardization, the prevalence (95% CI) of the lowest, lower, mid, higher, and highest risk groups was 0.18% (0.05–0.69), 2.12% (1.41–3.18), 2.37% (1.67–3.35), 4.00% (3.05–5.22), and 6.44% (5.24–7.90), respectively. (Table 2) The odds ratio between the lowest and highest risk groups was 38.50 (8.78–169.0). As to the linear trend of the predicted score, the extended Mantel Haenszel Chi Square value was calculated as 78.88, and the p-value was < 0.001. The adjusted proportions of IA patients in the higher and highest risk groups were 26.7% and 44.5%, respectively. The median risk scores among the IA patients and normal participants were 74 (IQR, 56–90) and 54 (IQR, 29–76), respectively (p < 0.001). The optimal cut-off risk score was 60.5 with 0.70 (95% CI, 0.69–0.70) of the area under the curve.

Annual medical expenditures covered by the NHIS for SAH was 120 billion Korean Won (KRW) in 2010, which had gradually increased by an annual growth rate of 7.74%, reaching 272 billion KRW in 2021.⁹ At the exchange rate of December 2022, it is approximately 215 million USD. Even so, this cost is limited to what is covered by the insurance, and the amount of non-insured treatment is not included. Moreover, SAH results in death and severe disability in > 50% of all patients. Considering not only the loss of the patient's labor force, but also the family's labor force sacrificed for the patient's care, the socio-economic loss due to SAH becomes even greater. Considering the high cost of SAH, there have been few researches regarding the epidemiology of IA. Although a few studies reported the prevalence of IAs as 2.8–7.0%, the study populations were limited to single institutes or provinces.^5–7 This is because, in most countries, there are no databases on the state of medical use at the national level. Using the national database of the Republic of Korea, the incidence and risk factors of IA were reported.¹ The estimated age-sex standardized incidence of IA was 52.2 per 100,000 person-year.

Unlike other critical diseases that are costly, SAH can be prevented if we can find IAs before rupture, because most spontaneous cases of SAH are caused by rupture of IA. However, considering together the estimated incidence and prevalence calculated from the participants who had undergone neurovascular imaging, most IA patients have been undiagnosed because they did not undergo appropriate examinations.^{1, 5–7} In fact, from 2008 to 2016, the number of national incident cases of unruptured IA had increased by 336.7%. The annual growth rate approached to 30%. Meanwhile, in the same time window, the number of SAH patients did not decrease, but rather increased by 2.4%.¹⁰ The rates of detection and treatment for unruptured IA seemed to be the tip of the iceberg to reduce the number of SAH. This also supports the idea that most IA patients are undiagnosed.

The guidelines for screening IA are as follows: 1) patients with two family members or more with IA or SAH should be offered aneurysmal screening by CTA or MRA. The risk factors that predict a particularly high risk of aneurysm occurrence in such families include history of hypertension, smoking, and female sex; and 2) patients with a history of autosomal dominant polycystic kidney disease (ADPKD), particularly those with a familial history of IA, should be offered to undergo screening by CTA or MRA, and it is reasonable to offer CTA or MRA to patients with coarctation of the aorta or microcephalic osteodysplastic primordial dwarfism.³ According to the familial history of IA, IA detection among first-degree relatives of those with sporadic SAH was approximately 4% (95% CI, 2.6–5.8), with a somewhat higher risk among siblings than among children of those affected.^11,12 According to a meta-analysis of unruptured IA, the adjusted prevalence ratio was 3.4 (95% CI, 1.9–5.9).¹³ However, considering the nationwide proportion of IA, populations with a familial history of IA are rare.¹⁰ Similarly, the reason ADPKD was included in the screening guideline is that it shows a high prevalence rate as compared to the general population. Among ADPKD patients, the prevalence of IA was estimated to be up to 13.4%.¹⁴ A previous meta-analysis reported that the prevalence ratio of ADPKD was 6.9 (95% CI, 3.5–14.0).¹³ However, the estimated prevalence of ADPKD, which is the most common hereditary kidney disease in Korea, was approximately 1/10,000.¹⁵ Moreover, only 0.3% of patients with unruptured IA had a history of polycystic kidney disease.¹⁶ Therefore, this criterion can cover only a few number of the population. Moreover, the abovementioned meta-analysis included only approximately 600 patients with unruptured IA for the adjusted analysis.

Thus, a strategy to identify participants at a high risk of developing IA among the general populations is needed, because neurovascular imaging is very expensive to carry out for the whole population, and there is always a risk of potential side effects due to the use of contrast agent. In a previous work, we proposed a risk assessment algorithm for IA using a machine learning method that utilized national healthcare examination data.⁴ The risk of IA was evaluated in quintile risk groups, and the highest risk group showed a 49.9 times higher risk of IA than the lowest risk group. The odds ratio of the highest risk group over the entire population was 4.7 (95% CI, 3.7–5.9). However, the limitation of the validation analysis in the previous work is that, due to the nature of the NHIS data, the analysis results include both those who underwent and those who did not undergo neurovascular examinations. To overcome these limitations and prove the algorithms value in actual clinical settings, it is important to validate the predictive power of the algorithm among participants who have performed neurovascular examinations for screening purposes.

In this study, we showed that the standardized prevalence of IA was 6.44% (95% CI, 5.24–7.90) in the highest risk group, which showed an odds ratio of 38.5 compared with 0.18% (95% CI, 0.05–0.69) in the lowest risk group. Moreover, the dose-response relation was also found to be statistically significant. The standardized odds ratio of the highest risk group over the entire participants was 3.0 (95% CI, 2.2–4.0). Although the adjusted odds of IA in the highest risk group was slightly lower than the items included in current guideline, the extent of coverage is much wider, because this risk assessment algorithm had been trained using data obtained from a national healthcare examination database of patients aged > 20 years without any restrictions in terms of medical and familial histories.

There are several limitations to our study. First, this study was designed as a retrospective validation; thus, we needed to consider potential biases. To overcome the population characteristics of those who visited CNUHH, a direct standardization was applied using data from a national database. As a result, age and sex distributions were statistically adjusted. Second, the diagnostic test performed was CTA. Although catheter angiography remains the gold standard for identifying IA, CTA shows comparable sensitivity and specificity along with MRA.¹⁷ Moreover, considering the high procedure-related risk of catheter angiography, it is not suitable for use for screening purposes. In this study, two radiology specialists cross-read the CTA to enhance the diagnostic accuracy.

In conclusion, we showed the clinical efficacy and validity of a prediction algorithm for risk estimation of IA using real-world data. Many IA cases with a high risk of rupture can be detected and treated before disaster using this strategy. In addition, as the occurrence of fatal SAH is decreased, it can contribute to minimizing the economic and social losses caused by this disease.

Acknowledgments

This study was supported by grant no. 16-2018-0006 from the Seoul National University Bundang Hospital Research Fund.

Data availability

The data are not available for public access because of patient privacy concerns but are available from the corresponding author on reasonable request approved by the institutional review boards of Chonnam National University Hwasun Hospital

Author contributions

T.K., W.J.P., S.C., Y.Y., H.K., C.W.O. and J.D.J. developed the study concept and design and extracted the data. T.K., J.C. and J.D.J. performed the statistical analysis. T.K. drafted the manuscript. J.C. composed all figures. All of authors revised the drafted manuscript. J.J.D and C.W.O had full access to all data in the study and takes responsibility for data integrity and data analysis accuracy. All authors reviewed the manuscript.

Competing interests

Tackeun Kim and Jin-Deok Joo hold stocks in TALOS Corp. All other authors have no competing interests.

Kim T, Lee H, Ahn S, et al. Incidence and risk factors of intracranial aneurysm: A national cohort study in Korea. Int J Stroke 2016;11(8):917–27.
Rinkel GJE, Algra A. Long-term outcomes of patients with aneurysmal subarachnoid haemorrhage. Lancet Neurol 2011;10(4):349–56.
Thompson BG, Brown RD Jr, Amin-Hanjani S, et al. Guidelines for the Management of Patients With Unruptured Intracranial Aneurysms: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 2015;46(8):2368–400.
Heo J, Park SJ, Kang S-H, Oh CW, Bang JS, Kim T. Prediction of Intracranial Aneurysm Risk using Machine Learning. Sci Rep 2020;10(1):6921.
Li M-H, Chen S-W, Li Y-D, et al. Prevalence of unruptured cerebral aneurysms in Chinese adults aged 35 to 75 years: a cross-sectional study. Ann Intern Med 2013;159(8):514–21.
Horikoshi T, Akiyama I, Yamagata Z, Nukui H. Retrospective analysis of the prevalence of asymptomatic cerebral aneurysm in 4518 patients undergoing magnetic resonance angiography--when does cerebral aneurysm develop? Neurol Med Chir 2002;42(3):105–12; discussion 113.
Jeon TY, Jeon P, Kim KH. Prevalence of unruptured intracranial aneurysm on MR angiography. Korean J Radiol 2011;12(5):547–53.
Lee J, Lee JS, Park S-H, Shin SA, Kim K. Cohort Profile: The National Health Insurance Service–National Sample Cohort (NHIS-NSC), South Korea. Int J Epidemiol 2016;46(2):e15–e15.
Healthcare Bigdata Hub by Health Insurance Review & Assessment Service [Internet]. [cited 2022 Dec 31];Available from: http://opendata.hira.or.kr/op/opc/olap3thDsInfo.do
Lee SU, Kim T, Kwon O-K, et al. Trends in the Incidence and Treatment of Cerebrovascular Diseases in Korea : Part I. Intracranial Aneurysm, Intracerebral Hemorrhage, and Arteriovenous Malformation. J Korean Neurosurg Soc 2020;63(1):56–68.
Risks and Benefits of Screening for Intracranial Aneurysms in First-Degree Relatives of Patients with Sporadic Subarachnoid Hemorrhage. N Engl J Med 1999;341(18):1344–50.
Raaymakers TWM, Group MS, Others. Aneurysms in relatives of patients with subarachnoid hemorrhage: frequency and risk factors. Neurology 1999;53(5):982–982.
Vlak MH, Algra A, Brandenburg R, Rinkel GJ. Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: a systematic review and meta-analysis. Lancet Neurol 2011;10(7):626–36.
Cagnazzo F, Gambacciani C, Morganti R, Perrini P. Intracranial aneurysms in patients with autosomal dominant polycystic kidney disease: prevalence, risk of rupture, and management. A systematic review. Acta Neurochir 2017;159(5):811–21.
Oh YK, Park HC, Ryu H, Kim Y-C, Oh K-H. Clinical and genetic characteristics of Korean autosomal dominant polycystic kidney disease patients. Korean J Intern Med 2021;36(4):767–79.
Japan Investigators U. The Natural Course of Unruptured Cerebral Aneurysms in a Japanese Cohort. N Engl J Med 2012;366(26):2474–82.
Chappell ET, Moure FC, Good MC. Comparison of computed tomographic angiography with digital subtraction angiography in the diagnosis of cerebral aneurysms: a meta-analysis. Neurosurgery 2003;52(3):624–31; discussion 630-1.

Competing interest reported. Tackeun Kim and Jin-Deok Joo hold stocks in TALOS Corp. All other authors have no competing interests.

Download PDF

Journal Publication

published 05 Sep, 2023

Read the published version in Scientific Reports →

Editorial decision: Major revision
14 May, 2023
Reviews received at journal
03 May, 2023
Reviewers agreed at journal
26 Apr, 2023
Reviewers invited by journal
26 Apr, 2023
Editor assigned by journal
23 Apr, 2023
Editor invited by journal
23 Apr, 2023
Submission checks completed at journal
23 Apr, 2023
First submitted to journal
16 Apr, 2023

You are reading this latest preprint version

Validation of Prediction Algorithm for Risk Estimation of Intracranial Aneurysm Development using Real-world Data

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Study Populations

Data collection

Inference with trained algorithm

Statistical Analyses

Results

Discussion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1