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Nik Abdull Halim NMH, Mohd Jamili AF, Che Dom N, Abd Rahman NH, Jamal Kareem Z, Dapari R

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Authors not listed · 2024

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Large-scale international health studies can identify subtle risks that smaller studies miss.

Plain English Summary

Summary written for general audiences

Researchers developed a risk prediction tool called the GSU-Pulmonary Score to estimate patients' chances of developing lung complications after elective surgery. The model was tested on over 123,000 patients across 114 countries and can accurately identify high-risk patients using ten simple factors available before surgery. This tool could help hospitals better allocate resources and prioritize patients during surgery recovery periods.

Why This Matters

While this surgical risk assessment study doesn't directly examine EMF exposure, it demonstrates something crucial for EMF health research: the power of large-scale, international data collection to identify subtle but significant health risks. The researchers analyzed over 123,000 patients across 114 countries to develop their predictive model. This is exactly the type of comprehensive approach we need for EMF health effects research. The reality is that many EMF studies involve small sample sizes or limited geographic scope, making it difficult to detect population-level health impacts. When we consider that everyone today carries multiple EMF-emitting devices and lives surrounded by wireless infrastructure, we need similarly robust, international studies to understand the full scope of EMF health effects. The medical community's commitment to evidence-based risk prediction for surgical outcomes should inspire the same rigorous approach to EMF exposure assessment.

Exposure Information

Specific exposure levels were not quantified in this study.

Cite This Study
Unknown (2024). Nik Abdull Halim NMH, Mohd Jamili AF, Che Dom N, Abd Rahman NH, Jamal Kareem Z, Dapari R.
Show BibTeX
@article{nik_abdull_halim_nmh_mohd_jamili_af_che_dom_n_abd_rahman_nh_jamal_kareem_z_dapari_r_ce3802,
  author = {Unknown},
  title = {Nik Abdull Halim NMH, Mohd Jamili AF, Che Dom N, Abd Rahman NH, Jamal Kareem Z, Dapari R},
  year = {2024},
  doi = {10.1016/S2589-7500(24)00065-7},
  
}
DOI: 10.1016/S2589-7500(24)00065-7PubMed: 38906616

Quick Questions About This Study

The study analyzed data from 123,512 patients across 1,884 hospitals in 114 countries, making it one of the largest international surgical outcome studies ever conducted for developing risk prediction models.
Lung complication rates varied by dataset: 2.0% in the main development group, 3.9% in the COVID-era validation group, and 4.7% in the pre-pandemic validation group from UK and Australasia.
The model showed good discrimination with area under the curve values of 0.773 in internal validation, 0.746 in COVID-era external validation, and 0.716 in pre-pandemic validation, indicating acceptable predictive accuracy.
LASSO regression performed similarly to XGBoost (0.786 vs 0.785 area under curve) but was chosen for the final model because it was more explainable and required fewer variables.
The study mentions ten predictor variables in the final model but doesn't list them specifically in the abstract. These are simple factors available before surgery to estimate pulmonary complication risk.