000170422 001__ 170422
000170422 005__ 20260420103355.0
000170422 0247_ $$2doi$$a10.1097/j.jcrs.0000000000001824
000170422 0248_ $$2sideral$$a148860
000170422 037__ $$aART-2025-148860
000170422 041__ $$aeng
000170422 100__ $$0(orcid)0000-0002-2839-9696$$aJiménez-García, Marta$$uUniversidad de Zaragoza
000170422 245__ $$aSegmentation of patients with cataract according to their ocular biometry: the LAKE classification
000170422 260__ $$c2025
000170422 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170422 5203_ $$aPurpose: To create a classification for cataractous eyes based on their ocular biometry and thus provide a standard to stratify eyes when evaluating intraocular lens (IOL) formulas performance in different groups. Setting: High-resolution cataract surgery unit. Design: Monocentric, observational, retrospective study. Methods: Data of 21 797 patients (both eyes explored) were acquired between 2016 and 2024. An exploratory analysis on the most relevant biometric variables determined the presence of uncommon eyes, justifying the need of a stratification. Eyes were classified in 27 subgroups based on axial length (AL), anterior chamber depth (ACD), and mean keratometry (Km). Subgroups were named with 3 letters corresponding to 3 levels—high (H), normal (N), or low (L)—for AL, ACD, and Km, respectively. Cutoffs were created on average ± SD. Linear mixed models were used to evaluate differences between groups, where α < 0.05 meant significant. Results: 43 954 eyes were classified, and all the subgroups contained at least 1 eye. The commonest subgroup was NNN (18 297 eyes, 41.97%), including eyes with (22.13 mm ≤ AL < 24.84 mm) AND (2.64 mm ≤ ACD < 3.43 mm) AND (42.50 diopters [D] ≤ Km < 45.60 D). Rarest subgroups were HLH and LHL (5 eyes and 1, respectively). Subgroups' characteristics (age, lens thickness [LT], LT/ACD, etc) reflect patterns seen frequently in clinical practice, for example, older patients with delayed surgeries, high LT and narrow ACD. Conclusions: There is no consensus on how to subdivide eyes when evaluating IOL power formulas performance. LAKE classification addresses this need by providing a systematic method for categorizing cataractous eyes based on their biometry.
000170422 540__ $$9info:eu-repo/semantics/embargoedAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000170422 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170422 700__ $$aPuzo, Martín
000170422 700__ $$aGiménez-Calvo, Galadriel$$uUniversidad de Zaragoza
000170422 700__ $$0(orcid)0000-0002-9250-9060$$aSegura-Calvo, Francisco J.$$uUniversidad de Zaragoza
000170422 700__ $$0(orcid)0000-0002-6745-7668$$aLarrosa Povés, Jose Manuel$$uUniversidad de Zaragoza
000170422 700__ $$aCastro-Alonso, Francisco J.$$uUniversidad de Zaragoza
000170422 700__ $$a
000170422 7102_ $$11013$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Oftalmología
000170422 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000170422 773__ $$g52, 4 (2025), 325-332$$pJ. cataract refract. surg.$$tJOURNAL OF CATARACT AND REFRACTIVE SURGERY$$x0886-3350
000170422 8564_ $$s6410768$$uhttps://zaguan.unizar.es/record/170422/files/texto_completo.pdf$$yVersión publicada$$zinfo:eu-repo/date/embargoEnd/2027-04-25
000170422 8564_ $$s1522224$$uhttps://zaguan.unizar.es/record/170422/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada$$zinfo:eu-repo/date/embargoEnd/2027-04-25
000170422 909CO $$ooai:zaguan.unizar.es:170422$$particulos$$pdriver
000170422 951__ $$a2026-04-18-10:49:17
000170422 980__ $$aARTICLE