By Dr Reza Lankarani, General Surgeon

Founder | Surgical Pioneering Newsletter and Podcast Series

Editorial Board Member | Genesis Journal of Surgery and Medicine

Data-driven surgery marks a crucial evolution in modern medicine, characterized by the use of measurable evidence and individualized patient metrics to guide surgical decision-making and optimize outcomes. This approach moves beyond generalized surgical protocols toward personalized risk management tailored to the patient’s specific anatomy, metabolic health, and long-term needs.

Key aspects of data-driven surgery, supported by recent research, include leveraging simple patient characteristics, challenging surgical dogma, utilizing predictive models, and adopting new technologies for customization:

1. Simple Data Informing Surgical Technique

In routine procedures, simple, measurable data is used to stratify risk and select the safest approach:

Subcutaneous Fat Thickness (SFT): Studies involving thousands of patients have established SFT (the thickness of the fat layer under the skin) as a powerful predictor of post-operative infection risk.

For patients with increased SFT undergoing a traditional open appendectomy, the risk of Surgical Site Infection (SSI) was found to be 1.82 times higher (nearly double).

Crucially, this data dictates a personalized approach: if the surgeon opts for the laparoscopic (keyhole) procedure, the SSI risk is not significantly higher even with increased SFT (risk increase of only 1.09x).

This provides a clear, actionable game plan: Measure SFT pre-operatively, and if it is high, strongly consider the minimally invasive route to lower the patient’s individual risk of infection.

2. Utilizing Predictive Modeling and Metabolic Markers

Data-driven surgery relies on sophisticated tools, including machine learning and metabolic markers, to predict individual patient risks:

AI Risk Calculators: Tools like the Potter Predictive Optimal Trees in Emergency Surgery risk calculator use machine learning to provide transparent, accurate, and actionable risk assessments for emergency laparotomy patients.

Validation studies demonstrated its high accuracy in predicting 30-day mortality (C statistic = 0.90) and complications like septic shock and respiratory failure.

This early warning allows clinicians to initiate proactive, targeted management sooner (e.g., optimizing fluids or considering antibiotics), potentially avoiding severe complications or high-risk surgeries later on.

Metabolic Markers: Metabolic health insights are integrated into pre-operative risk profiles, even in oncology. The Triglyceride Glucose (TAIG) index, which correlates highly with insulin resistance, is being investigated as a potential diagnostic aid.

The TAIG index has shown a moderate ability (pooled area under the curve of 0.64) to help differentiate between malignant (cancerous) and benign (non-cancerous) breast lesions.

Although not strong enough to be a standalone diagnostic tool, combining this metabolic data with imaging and biopsy findings helps create a more comprehensive and personalized risk profile for the individual patient before surgery.

3. Data Challenging Surgical Dogma

Data is essential for maintaining surgical integrity and ensuring the best long-term outcome for the patient, even if the evidence contradicts the prevailing dogma of choosing the least invasive option:

The Thymus Gamble: In the case of aggressive chest tumors (thymic carcinoma), data showed that pre-operative scans were highly unreliable, with almost 80% of tumors appearing early stage actually being more advanced upon removal.

Researchers found that patients who received the less invasive partial removal had significantly worse overall survival rates (dropping from 67% to 30%) compared to those who received the more extensive total thymectomy.

In this high-stakes scenario, the evidence makes it clear that total thymectomy is "oncologically superior," demonstrating that the choice must be guided by definitive, hard, long-term evidence—even if it means opting for a bigger, more aggressive operation.

4. Data Reframing Major Surgery as Prevention

Large-scale, long-term data sets are used to evaluate the impact of surgery as a proactive, durable risk mitigation strategy:

Bariatric Surgery: Data supports reframing bariatric surgery less as a last-resort treatment for obesity and more as a proactive defense against a lifetime of chronic illness, offering a systemic metabolic reset.

A massive study showed that the surgery group had a nearly 80% lower risk of developing Type 2 Diabetes later on, and substantially reduced risks for hypertension (almost 59% lower), high cholesterol (50.5% lower), and obstructive sleep apnea (nearly 57% lower) compared to the control group.

This data provides a durable approach for risk mitigation and supports considering surgery as a potential "best first move" for carefully selected patients.

5. Data Guiding Real-Time Customization

The ultimate form of data-driven surgery involves customizing solutions at the moment of the procedure using advanced technology:

Portable 3D Bioprinting: Handheld 3D printers, often called a "bone glue gun," allow surgeons to extrude a custom scaffold directly into complex bone defects.

The "bio-ink" material (a composite of polycaprolactone, PCL, and hydroxyapatite, HA) can be customized on the fly.

The surgeon can adjust the ratio of PCL-to-HA to tune the final scaffold's properties, such as its mechanical strength and how quickly it degrades, directly addressing the unique load-bearing requirements and specific biological needs of that particular bone defect in that patient.

By Dr Reza Lankarani, General Surgeon

Founder | Surgical Pioneering Newsletter and Podcast Series

Editorial Board Member | Genesis Journal of Surgery and Medicine

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