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.

Pulmonary hypertension in lymphangioleiomyomatosis: prevalence, severity and the role of carbon monoxide diffusion capacity as a screening method.

BACKGROUND: Lymphangioleiomyomatosis (LAM) is included within group 5 of the current PH classification (unclear multifactorial mechanisms). However, data regarding the occurrence of PH in LAM are scarce. The aims of the study were to describe the prevalence and characteristics of PH in a large cohort of LAM patients with different levels of severity, and to evaluate the role of echocardiography and carbon monoxide diffusion capacity (DLCO) as screening methods for PH in LAM. METHODS: One hundred five LAM patients underwent transthoracic echocardiography, pulmonary function tests (PFTs) and 6-min walk test (6MWT). Patients with a suspicion of PH on echocardiography, defined by the presence of estimated systolic pulmonary artery pressure (PAP) over 35 mmHg or PFT showing DLco below 40% of the predicted value, underwent right heart catheterisation to confirm the diagnosis of PH. RESULTS: Eight patients (7.6%) had PH confirmed on right heart catheterisation, six patients (5.7%) had a pre-capillary pattern and two patients (1.9%) had a post-capillary profile. Only one patient (1%) had mean PAP over 35 mmHg. Patients with PH had lower FEV1 and DLCO in PFTs and greater oxygen desaturation and dyspnea intensity during 6MWT compared with those without PH. In 63% of the patients with confirmed PH, the right heart catheterisation was performed based only on DLCO result. CONCLUSIONS: The prevalence of PH is low in LAM patients. Pulmonary hypertension in LAM is typically mild and significantly associated with pulmonary parenchymal involvement. Carbon monoxide diffusion capacity significantly improved the identification of PH in LAM patients.

Pulmonary rehabilitation in lymphangioleiomyomatosis: a controlled clinical trial.

Lymphangioleiomyomatosis (LAM) is a cystic lung disease frequently associated with reduced exercise capacity. The aim of this study was to assess safety and efficacy of pulmonary rehabilitation in LAM.This controlled clinical trial included 40 patients with LAM and a low physical activity level. The pulmonary rehabilitation programme comprised 24 aerobic and muscle strength training sessions and education. The primary outcome was exercise capacity (endurance time during a constant work rate exercise test). Secondary outcomes included health-related quality of life (St George's Respiratory Questionnaire (SGRQ)), 6-min walking distance (6MWD), dyspnoea, peak oxygen consumption (V'O2 ), daily physical activity (pedometer), symptoms of anxiety and depression, lung function and peripheral muscle strength (one-repetition maximum).The baseline characteristics were well balanced between the groups. The pulmonary rehabilitation group exhibited improvements in the following outcomes versus controls: endurance time (median (interquartile range) 169 (2-303) s versus -33 (-129-39) s; p=0.001), SGRQ (median (interquartile range) -8 (-16-2) versus 2 (-4-5); p=0.002) and 6MWD (median (interquartile range) 59 (13-81) m versus 20 (-12-30) m; p=0.002). Dyspnoea, peak V'O2 , daily physical activity and muscle strength also improved significantly. No serious adverse events were observed.Pulmonary rehabilitation is a safe intervention and improves exercise capacity, dyspnoea, daily physical activity, quality of life and muscle strength in LAM.