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A Smart Computer Program to Assist Healthcare Providers in Selecting the Best Treatment for Patients with Urinary Stones

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Artificial Intelligence Models, Tools and Applications

Submitted:

30 May 2024

Posted:

31 May 2024

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Abstract
Urinary tract stones can obstruct the urinary tract, resulting in severe lower back pain, blood in the urine, vomiting, and painful urination. While small stones can pass naturally through the urine stream, larger ones necessitate fragmentation with shock wave lithotripsy (SWL) or laser ureteroscopy (URS) to prevent urinary tract blockage. A smart computer program was developed to predict treatment efficacy for removing a stone and potential complications for individual patients based on factors such as age, health conditions, stone details, and treatment instruments, ultimately recommending the most suitable option. Here, I present three case studies and their predicted outcomes. The program is available at: http://peteranoble.com/webapps.html
Keywords: 
Subject: Public Health and Healthcare  -   Primary Health Care

Introduction

Stones in the urinary tract are becoming more common worldwide, likely due to shifts in dietary habits and climate change1. These stones form when certain substances in the urine, such as calcium, oxalate, and/or uric acid, become highly concentrated and clump together in the urinary tract. While small stones, typically around 4 millimeters or less, can pass out of the body through the urine stream, larger ones may become lodged in the urinary system, causing symptoms like severe lower back pain, blood in the urine, vomiting, and painful urination. It is estimated that approximately 11% of people in the United States will experience a urinary tract stone at some point in their lives2.
Physicians use shock wave lithotripsy (SWL) or laser ureteroscopy (URS) to fragment stones, facilitating their passage out of the body while minimizing treatment complications for patients, such as pain and bleeding. SWL delivers strong shock waves to the stone from outside the body, without causing harm to internal organs3. In contrast, URS is a more invasive procedure4, involving the insertion of a small, flexible tube with a camera into the urinary tract to locate the stone. Subsequently, a laser beam inside the tube fragments the stone into small pieces5.
The effectiveness of SWL and URS treatments varies depending on factors such as the patient's health, age, body size, and the size, type, and location of the urinary stone6-10. For example, URS may entail more treatment complications and higher costs, sometimes requiring extended hospital stays compared to SWL11-12. Most patients tend to prefer SWL13. While a recent review by the National Institute of Health suggests that URS marginally outperforms SWL, SWL is generally considered more effective and cost-efficient14. Selecting the ‘optimal’ treatment for a patient is therefore not straightforward; an approach that helps physicians with these decisions is highly desired.
Using anonymous data accessible from the Kidney Stone Registry (http://kidneystoneregistry.com.s3-website-us-west-2.amazonaws.com/), I analyzed the treatment outcomes from 17,242 patients who have undergone SWL or URS treatments at multiple sites across the United States. The anonymous dataset lacks identifiable information, ensuring no possible linkage to personal data. Details of the approach and quality assessment of the AI models used to build the smart program can be found here: Refs. 15 and 16.

Results

A smart computer program15 predicted the efficacy of stone fragmentation treatments and assessed potential post-treatment health issues. The program considered various factors, including age, sex, weight, presence of health conditions like diabetes, prior medication use, and details about the stone such as size and location. Additionally, it took into account the type of machines used for SWL and URS treatments.
The computer program produced four predictions: the likelihood of fragmenting the stone to 4 millimeters or less for SWL and URS, as well as the probability of treatment-related health issues for each method. The output for each prediction consisted of the average and standard deviation from 10 independently trained Artificial Intelligence (AI) models.
Additionally, the program recommended the most suitable treatment for an individual patient based on the average and standard deviation of the models, as specified in the original study16. Three examples are shown below.

Case Presentations

Case 1

Patient: A 29-year-old woman with BMI of 24 kg/m2 and no health problems.
Stone: In her left kidney, 14mm long and 8mm wide.
Medications: She has not taken any blood thinners.
Machines: Dornier Compact Sigma (SWL) and Lumenis Versapulse 100 watt (URS).
Web interface for input data of this patient (Figure 1)15.
The program output (Figure 2):
- SWL: 69.4% chance of breaking up the stone, 11.2% chance of problems.
- URS: 61.1% chance of breaking up the stone, 5.5% chance of problems.
- Recommendation: URS is better, with fewer expected problems.

Case 2

Patient: A 45-year-old man with BMI of 28 kg/m2 and no health problems.
Stone: In his right kidney, 10mm long and 10mm wide.
Medications: He has not taken any blood thinners.
Machines: Storz SLX-T (SWL) and Odyssey Convergent 30 watt (URS).
The program output (Figure 3):
- SWL: 94.7% chance of breaking up the stone, 2.4% chance of problems.
- URS: 100% chance of breaking up the stone, 0% chance of problems.
- Recommendation: Both SWL and URS are preferred options.

Case 3

Patient: A 75-year-old woman with BMI of 30 kg/m2 and health problems.
Stone: In her left ureter, 9mm long and 9mm wide.
Medications: She has not taken any blood thinners.
Machines: Storz F2 (SWL) and Lumenis Versapulse 20 watt (URS).
The program output (Figure 4):
- SWL: 62% chance of breaking up the stone, 3.1% chance of problems.
- URS: 84.9% chance of breaking up the stone, 55.3% chance of problems.
- Recommendation: SWL is better, with fewer expected problems.

Discussion

The motivation of the study was to demonstrate the utility of a smart computer program that predicts SWL and URS outcomes, aiding healthcare professionals in patient care decisions. Our study is unique from other studies because the interventions took place at multiple institutions (n = 41+) by different medical professionals (n = 41+) using a variety of SWL and URS instruments. Hence, the results should be generalizable and not specific to a particular institution or healthcare professional. While there are specific guidelines for the management of urolithiasis set by the American Urological Association (AUA) and European Association of Urologists (EAU), our study provides recommendations based on past treatments that, in theory, should align with these guidelines.
The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively16. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome.

Conclusions

The smart computer program represents a groundbreaking advancement in predicting stone treatment outcomes for individual patients having a urinary stone. It leverages data from multiple institutions and diverse physicians, analyzing thousands of patients.

Acknowledgement

None.

Conflict of Interest

None.

References

  1. Romero V, Akpinar H, Assimos DG. Kidney stones: A global picture of prevalence, incidence, and associated risk factors. Rev Urol. 2010 Spring;12(2-3):e86-96. [PubMed] [PubMed Central]
  2. Scales CD Jr, Smith AC, Hanley JM, Saigal CS; Urologic Diseases in America Project. Prevalence of kidney stones in the United States. Eur Urol. 2012 Jul;62(1):160-5. Epub 2012 Mar 31. [CrossRef] [PubMed] [PubMed Central]
  3. McAteer JA, Evan AP. The acute and long-term adverse effects of shock wave lithotripsy. Semin Nephrol. 2008 Mar;28(2):200-13. [CrossRef] [PubMed] [PubMed Central]
  4. Rodr’guez D, Sacco DE. Minimally invasive surgical treatment for kidney stone disease. Adv Chronic Kidney Dis. 2015 Jul;22(4):266-72. [CrossRef] [PubMed]
  5. Chew BH, Zavaglia B, Paterson RF, Teichman JM, Lange D, Zappavigna C, Matlaga BR, Nunez-Nateras R, Bruhn A, Altamar HO, Humphreys MR, Shah O, Miller NL. A multicenter comparison of the safety and effectiveness of ureteroscopic laser lithotripsy in obese and normal weight patients. J Endourol. 2013 Jun;27(6):710-4P. MID: 23521213. [CrossRef]
  6. Joshi HB, Johnson H, Pietropaolo A, Raja A, Joyce AD, Somani B, Philip J, Biyani CS, Pickles T. Urinary Stones and Intervention Quality of Life (USIQoL): Development and Validation of a New Core Universal Patient-reported Outcome Measure for Urinary Calculi. Eur Urol Focus. 2021 Jan 8:S2405-4569(20)30313-8. Epub ahead of print. [CrossRef] [PubMed]
  7. Moudi E, Hosseini SR, Bijani A. Nephrolithiasis in elderly population; effect of demographic characteristics. J Nephropathol. 2017 Mar;6(2):63-68. Epub 2016 Dec 17. [CrossRef] [PubMed] [PubMed Central]
  8. Krambeck AE, Lieske JC, Li X, Bergstralh EJ, Melton LJ 3rd, Rule AD. Effect of age on the clinical presentation of incident symptomatic urolithiasis in the general population. J Urol. 2013 Jan;189(1):158-64. Epub 2012 Nov 16. [CrossRef] [PubMed] [PubMed Central]
  9. Mains EAA, Blackmur JP, Sharma AD, Gietzmann WK, El-Mokadem I, Stephenson C, Wallace S, Phipps S, Thomas BG, Tolley DA, Cutress ML. Shockwave Lithotripsy Is an Efficacious Treatment Modality for Obese Patients with Upper Ureteral Calculi: Logistic Regression and Matched-Pair Analyses from a Dedicated Center Comparing Treatment Outcomes by Skin-to-Stone Distance. J Endourol. 2020 Apr;34(4):487-494. Epub 2020 Mar 27. [CrossRef] [PubMed]
  10. Raja A, Hekmati Z, Joshi HB. How Do Urinary Calculi Influence Health-Related Quality of Life and Patient Treatment Preference: A Systematic Review. J Endourol. 2016 Jul;30(7):727-43. Epub 2016. [CrossRef] [PubMed]
  11. Constanti M, Calvert RC, Thomas K, Dickinson A, Carlisle S. Cost analysis of ureteroscopy (URS) vs extracorporeal shockwave lithotripsy (SWL) in the management of ureteric stones <10 mm in adults: A UK perspective. BJU Int. 2020 Mar;125(3):457-466. [CrossRef] [PubMed]
  12. Aboumarzouk OM, Kata SG, Keeley FX, McClinton S, Nabi G. Extracorporeal shock wave lithotripsy (SWL) versus ureteroscopic management for ureteric calculi. Cochrane Database Syst Rev. 2012 May 16;(5):CD006029. [CrossRef] [PubMed]
  13. Sarkissian C, Noble M, Li J, Monga M. Patient decision making for asymptomatic renal calculi: Balancing benefit and risk. Urology. 2013 Feb;81(2):236-40. [CrossRef] [PubMed]
  14. National Institute for Health and Care Excellence. 2019. Surgical Treatment Intervention Evidence Review. NICE guidline NG118.
  15. http://peteranoble.com/webapps.html.
  16. Noble PA, Hamilton BD, Gerber G. Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients. PLoS ONE. 2024 May 2;19(5):e0301812. [CrossRef] [PubMed] [PubMed Central]
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Figure 1. Screenshot of web interface displaying input data for Case 1 patient.
Figure 1. Screenshot of web interface displaying input data for Case 1 patient.
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Figure 2. Average and standard deviation of predictions for stone fragmentation success (<=4mm) and treatment complications based on output from ten AI models for each prediction. URS has fewer treatment complications than SWL and is the preferred option.
Figure 2. Average and standard deviation of predictions for stone fragmentation success (<=4mm) and treatment complications based on output from ten AI models for each prediction. URS has fewer treatment complications than SWL and is the preferred option.
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Figure 3. Average and standard deviation of predictions for stone fragmentation success (<=4mm) and treatment complications based on output from ten AI models for each prediction. Both SWL or URS are preferred options.
Figure 3. Average and standard deviation of predictions for stone fragmentation success (<=4mm) and treatment complications based on output from ten AI models for each prediction. Both SWL or URS are preferred options.
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Figure 4. Average and standard deviation of predictions for stone fragmentation success (<=4mm) and treatment complications based on output from ten AI models for each prediction. Both SWL is the preferred option because it has a lower probability of treatment complications than URS. .
Figure 4. Average and standard deviation of predictions for stone fragmentation success (<=4mm) and treatment complications based on output from ten AI models for each prediction. Both SWL is the preferred option because it has a lower probability of treatment complications than URS. .
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