Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects / Uso de aprendizado de máquina para predizer o risco de picos de pressão intraocular no início da manhã em pacientes com glaucoma e suspeitos

ABSTRACT Purpose: To use machine learning to predict the risk of intraocular pressure peaks at 6 a.m. in primary open-angle glaucoma patients and suspects. Methods: This cross-sectional observational study included 98 eyes of 98 patients who underwent a 24-hour intraocular pressure curve (including the intraocular pressure measurements at 6 a.m.). The diurnal intraocular pressure curve was defined as a series of three measurements at 8 a.m., 9 a.m., and 11 a.m. from the 24-hour intraocular pressure curve. Two new variables were introduced: slope and concavity. The slope of the curve was calculated as the difference between intraocular pressure measurements at 9 a.m. and 8 a.m. and reflected the intraocular pressure change in the first hour. The concavity of the curve was calculated as the difference between the slopes at 9 a.m. and 8 a.m. and indicated if the curve was bent upward or downward. A classification tree was used to determine a multivariate algorithm from the measurements of the diurnal intraocular pressure curve to predict the risk of elevated intraocular pressure at 6 a.m. Results: Forty-nine (50%) eyes had intraocular pressure measurements at 6 a.m. >21 mmHg, and the median intraocular pressure peak in these eyes at 6 a.m. was 26 mmHg. The best predictors of intraocular pressure measurements >21 mmHg at 6 a.m. were the intraocular pressure measurements at 8 a.m. and concavity. The proposed model achieved a sensitivity of 100% and a specificity of 86%, resulting in an accuracy of 93%. Conclusions: The machine learning approach was able to predict the risk of intraocular pressure peaks at 6 a.m. with good accuracy. This new approach to the diurnal intraocular pressure curve may become a widely used tool in daily practice and the indication of a 24-hour intraocular pressure curve could be rationalized according to risk stratification.

RESUMO Objetivo: Utilizar aprendizado de máquina para predizer o risco de picos de pressão intraocular às 6 AM em pacientes com glaucoma primário de ângulo aberto e suspeitos. Métodos: Esse estudo observacional transversal incluiu 98 olhos de 98 pacientes submetidos à curva de 24 horas de pressão intraocular (incluindo as medidas às 6 AM). A curva diurna de pressão intraocular foi definida como uma série de três medidas da curva de 24 horas de pressão intraocular às 8 AM, às 9 AM e às 11 AM. Duas novas variáveis foram apresentadas: inclinação e concavidade. A inclinação da curva às 8 AM foi calculada como a diferença entre pressão intraocular às 9 AM e 8 AM e reflete a variação da pressão intraocular na primeira hora. A concavidade da curva foi calculada como a diferença entre as inclinações às 9 AM e às 8 AM e pode ser para cima ou para baixo. Uma árvore de classificação foi usada para determinar um algoritmo multivariado a partir das medidas da curva diurna para prever o risco de pressão intraocular elevada às 6 AM. Resultados: Quarenta e nove (50%) olhos apresentaram pressão intraocular às 6 AM >21 mmHg e a mediana do pico de pressão intraocularPIO foi 26 mmHg. Os melhores preditores de pressão intraocular às 6 AM >21 mmHg foram a pressão intraocular às 8 AM e a concavidade. O modelo proposto apresentou uma sensibilidade de 100% e uma especificidade de 86%, com uma acurácia de 93%. Conclusões: A abordagem de aprendizado de máquina foi capaz de prever o risco de picos de pressão intraocular às 6 AM com uma boa acurácia. Essa nova abordagem para a curva diurna de pressão intraocular pode se tornar uma ferramenta amplamente utilizada na prática clínica e a indicação da curva de 24 horas de pressão intraocular pode ser racionalizada de acordo com a estratificação de risco.

Use of machine learning to predict the risk of early morning intraocular pressure peaks in glaucoma patients and suspects.

PURPOSE: To use machine learning to predict the risk of intraocular pressure peaks at 6 a.m. in primary open-angle glaucoma patients and suspects. METHODS: This cross-sectional observational study included 98 eyes of 98 patients who underwent a 24-hour intraocular pressure curve (including the intraocular pressure measurements at 6 a.m.). The diurnal intraocular pressure curve was defined as a series of three measurements at 8 a.m., 9 a.m., and 11 a.m. from the 24-hour intraocular pressure curve. Two new variables were introduced: slope and concavity. The slope of the curve was calculated as the difference between intraocular pressure measurements at 9 a.m. and 8 a.m. and reflected the intraocular pressure change in the first hour. The concavity of the curve was calculated as the difference between the slopes at 9 a.m. and 8 a.m. and indicated if the curve was bent upward or downward. A classification tree was used to determine a multivariate algorithm from the measurements of the diurnal intraocular pressure curve to predict the risk of elevated intraocular pressure at 6 a.m. RESULTS: Forty-nine (50%) eyes had intraocular pressure measurements at 6 a.m. >21 mmHg, and the median intraocular pressure peak in these eyes at 6 a.m. was 26 mmHg. The best predictors of intraocular pressure measurements >21 mmHg at 6 a.m. were the intraocular pressure measurements at 8 a.m. and concavity. The proposed model achieved a sensitivity of 100% and a specificity of 86%, resulting in an accuracy of 93%. CONCLUSIONS: The machine learning approach was able to predict the risk of intraocular pressure peaks at 6 a.m. with good accuracy. This new approach to the diurnal intraocular pressure curve may become a widely used tool in daily practice and the indication of a 24-hour intraocular pressure curve could be rationalized according to risk stratification.

Late needling of flat filtering blebs with adjunctive mitomycin C: efficacy and safety for the corneal endothelium.

PURPOSE: To assess the efficacy of needle revision using mitomycin C (MMC) for reviving failed filtering blebs during the late postoperative period and its safety for the corneal endothelium. DESIGN: Prospective interventional case series. PARTICIPANTS: We recruited 125 eyes from 98 patients with uncontrolled glaucoma. All had at least 1 failed trabeculectomy, a flat filtering bleb, and a patent internal ostium on gonioscopy. The average time between the last trabeculectomy and needle revision was 5.3±5.3 years (range, 6 months to 30 years). INTERVENTION: Needling revision with a subconjunctival injection of 8 µg of MMC. MAIN OUTCOME MEASURES: Intraocular pressure (IOP) and number of hypotensive medications at latest visit, central corneal thickness (CCT), corneal endothelial cell density (ECD), coefficient of variation of cell size (CV), and hexagonality before and after needling. RESULTS: We performed 186 needling procedures on 125 eyes (mean, 1.5±0.6 procedures/eye). Seventy-three eyes (58.4%) were needled once, 44 eyes (35.2%) were needled twice, 7 eyes (5.6%) were needled 3 times, and 1 eye (0.8%) was needled 4 times. We reestablished aqueous flow and obtained a raised bleb in 115 eyes (92%). After an average follow-up of 20.8±12.0 months, the mean IOP decreased from 20.1±5.2 mmHg preoperatively to 13.2±6.8 mmHg (P<0.001), and the mean number of hypotensive medications per eye decreased from 2.4±1.1 to 0.8±1.3 (P<0.001) at the latest visit. The overall success rate (IOP ≤16 mmHg) was 76% (58.4% without medication and 17.6% with hypotensive medications). Kaplan-Meier survival rates were 91.2% at the 1-month follow-up, 84.5% at 6 months, 81.0% at 1 year, 74.6% at 2 years, and 66.3% at 3 years. We studied the corneal endothelium in 42 eyes of 36 patients. There was no statistically significant difference between preoperative CCT, ECD, CV, or hexagonality and postoperative measurements at 1 week and 1, 3, 6, and 12 months. CONCLUSIONS: Needling revision with adjunctive MMC is effective for reviving flat filtering blebs and controlling IOP, even several years after the original trabeculectomy, and seems to be safe for the corneal endothelium.