Key result
Evidence-based optimization of all three Haigis formula constants (a0/a1/a2) reduced the mean absolute prediction error to 0.19 D compared to 0.44 D with non-optimized constants.
Why the study?
While competency-based assessments of resident cataract surgery are standard, evidence-based analysis of refractive outcomes remains underutilized in educational curricula.
Does evidence-based optimization of IOL constants improve refractive outcomes in patients undergoing cataract surgery by novice resident surgeons?
Cohort (n=431)
No
Does evidence-based optimization of IOL constants improve refractive outcomes in patients undergoing cataract surgery by novice resident surgeons?
Formal evidence-based optimization of IOL constants significantly improves refractive prediction accuracy for novice resident cataract surgeons, achieving results comparable to advanced prediction models.
Answers are grounded in this paper and cite their sources.
May improve refractive accuracy for novice surgeons; supports optimization but leaves open prospective validation.
Evidence based practice enhances healthcare delivery and prevents unsafe procedures. While competency based assessments of resident cataract surgery are standard, evidence based analysis of refractive outcomes remains underutilized in educational curricula. This retrospective single center study evaluated refractive outcomes from 21 novice ophthalmology resident surgeons. Three independent groups were compared based on formal constant optimization for intraocular lens (IOL) calculation: non-optimized Haigis (n = 216), a0-optimized (n = 94), and a0/a1/a2-optimized (n = 121). All surgeries were supervised by a single attending surgeon. Mean absolute error (MAE) and the percentage of eyes within ±0.25 D and ±0.50 D of predicted spherical equivalent (SEQ) were calculated. Also, systematic bias in effective lens position (ELP) was analyzed to update manufacturer IOL constants. MAE improved from 0.44 D (non-optimized) to 0.35 D (a0-optimized p = 0.009) and 0.19 D (a0/a1/a2-optimized p < 0.001). The percentage within ±0.50 D increased from 65.7% to 74.4% to 95.0%, respectively. With ELP bias correction, updated A constant and ACD were 119.266 and 5.755 mm. a0/a1/a2-optimized outcomes were comparable to ELP bias correction for the Barrett UII, Kane, and Hill-RBF formulas. Evidence based optimization of IOL constants significantly enhances novice resident surgical outcomes, achieving parity with prediction models. A formal curriculum on IOL calculation and optimization is warranted.
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Patel et al. (2025) conducted a cohort in Cataract (n=431). a0/a1/a2-optimized Haigis formula (Evidence-based optimization of IOL constants) vs. Non-optimized default Haigis formula was evaluated on Mean absolute error (MAE) of predicted spherical equivalent (SEQ) (p=<0.001). Evidence-based optimization of all three Haigis formula constants (a0/a1/a2) reduced the mean absolute prediction error to 0.19 D compared to 0.44 D with non-optimized constants.
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