A Comparison and Validation of Predicted Continuous Positive Airway Pressure Formulas vs Autotitration

Background: Continuous positive airway pressure (CPAP) is the treatment of obstructive sleep apnea (OSA). The CPAP pressure is generally estimated by manual titration or an auto-CPAP device. An alternative method involves the use of the predictive equation. There is limited study available for the Indian population. Objectives: To compare CPAP pressure obtained by mathematical formulas with auto-CPAP titration and formulation, a preliminary predictive equation from derived data to be validated with titrated CPAP. Methods and materials: A retrospective observational study was performed in 130 patients in Department of Pulmonary Medicine of our institute from April 2019 to July 2021. Detailed history, anthropometric parameters, whole-night level-II polysomnography (PSG), and CPAP titration were performed. Stepwise linear regression was applied to establish predictive equation. This equation results were compared with available other equations and autotitrated readings. Results: The mean (SD) of age, BMI, neck girth, Epworth score, lowest SPO2 (%), and AHI was 56.72 (11.31), 33.87 (6.43), 39.7 (4.46), 17.75 (3.18), 84.65 (8.44), and 48.75 (21.09), respectively, with male杅emale ratio of 3:2. Mild, moderate, and severe OSA were 7 (5.4%), 18 (13.8%), and 105 (80.8%), respectively. Continuous positive airway pressure obtained from equations was in the range of 7.40�.95 cm H2O. Obtained readings by equations showed a comparable correlation with CPAP-titrated results (p <0.001). Conclusion: The optimum titration pressure correlates with pressure derived from the predictive equation that is predicted average therapeutic CPAP pressure = 3.98 + 0.065 (ODI) + (AHI) + 0.018 (nadir SPO2) - 0.013 (NC).

Establishment of a predictive equation for pulmonary ventilation function in school-aged children in northeast China: a prospective study / 中国当代儿科杂志

OBJECTIVES@#To establish a predictive equation for commonly used pulmonary ventilation function parameters in children aged 6-<16 years in northeast China.@*METHODS@#A total of 504 healthy children from Liaoning, Jilin, and Heilongjiang provinces of China were selected for the prospective study, among whom there were 242 boys and 262 girls. The JAEGER MasterScreen Pneumo spirometer was used to measure pulmonary ventilation function. With the measured values of 10 parameters, including forced vital capacity (FVC), forced expiratory volume in 1 second (FEV@*RESULTS@#The boys aged 9-<10 years and 15-<16 years had significantly higher body height, FVC, and FEV@*CONCLUSIONS@#A new predictive equation for the main pulmonary ventilation function parameters has been established in this study for children aged 6-<16 years in northeast China, which provides a basis for accurate judgment of pulmonary function abnormalities in clinical practice.

Maximal power output estimates the MLSS before and after aerobic training / Potência máxima estima a MFEL antes e após o treinamento aeróbio / Potencia máxima estima el MLSS antes y después del entrenamiento aerobio

The purpose of this study is to present an equation to predict the maximal lactate steady state (MLSS) through a VO2peak incremental protocol. Twenty-six physically active men were divided in two groups (G1 and G2). They performed one maximal incremental test to determine their VO2peak and maximal power output (Wpeak), and also several constant intensity tests to determine MLSS intensity (MLSSw) on a cycle ergometer. Group G2 underwent six weeks of aerobic training at MLSSw. A regression equation was created using G1 subjects Wpeak and MLSSw to estimate the MLSS intensity (MLSSweq) before and after training for G2 (MLSSweq = 0.866 x Wpeak-41.734). The mean values were not different (150±27W vs 148±27W, before training / 171±26W vs 177±24W, after training) and significant correlations were found between the measured and the estimated MLSSw before (r²=0.49) and after training (r²=0.62) in G2. The proposed equation was effective to estimate the MLSS intensity before and after aerobic training...

Este estudo propõe uma equação para predição da máxima fase estável de lactato (MFEL) através de um teste para medida do VO2pico. Vinte e seis homens fisicamente ativos foram divididos em dois grupos (G1 e G2). Eles realizaram um teste máximo para medida do VO2pico e potência máxima (Ppico) e testes submáximos para determinar a intensidade da MFEL (MFELw) em cicloergômetro. O grupo G2 treinou por seis semanas na MFELw. Uma equação de regressão linear foi desenvolvida utilizando os resultados do G1 (Ppico e MFELw) para estimativa da MFEL (MFELweq) antes e após o treinamento no G2 (MFELweq=0,866 x Ppico-41,734). Os valores médios não foram diferentes (150±27W vs 148±27W, pré-treino / 171±26W vs 177±24W, pós-treino) e encontrou-se uma correlação significativa entre a MFELw medida e estimada antes (r²=0,49) e após o treinamento (r²=0,62) no grupo G2. A equação proposta foi efetiva para estimar a MFEL antes e após o treinamento...

Este estudio propone una ecuación para la predicción del máximo estado estable de lactato (MLSS) a través de un test para medir VO2pico. Veintiséis hombres físicamente activos se dividieron en dos grupos (G1 y G2). Ellos realizaron un test máximo para medir el VO2pico y potencia máxima (Ppico) y testes submáximas para determinar la intensidad de la MLSS (MLSSw) en cicloergómetro. El grupo G2 entrenó por seis semanas en la MLSSw. Una ecuación de regresión lineal fue desarrollada utilizando los resultados del G1 (Ppico y MLSSw) para estimar la MLSS (MLSSweq) antes y después del entrenamiento en el G2 (MFELweq=0,866 x Ppico-41,734). Los valores medios no fueron distintos (150±27W vs 148±27W, pre-entrenamiento / 171±26W vs 177±24W, después del entrenamiento) y se encontró una correlación significativa entre la MFELw medida y estimada antes (r²=0,49) y después del entrenamiento (r²=0,62) en el G2. La ecuación propuesta fue eficaz para estimar la MLSS antes y después del entrenamiento...

Predição da potência aeróbia (VO2máx) de crianças e adolescentes em teste incremental na esteira rolante / Prediction of aerobic power (VO2max) of children and adolescents during an incremental treadmill test

O consumo máximo de oxigênio (VO2máx) é a quantidade máxima de energia que pode ser produzida pelo metabolismo aeróbio em determinada unidade de tempo, podendo ser determinado direta ou indiretamente através de equações preditivas. O objetivo deste trabalho foi elaborar uma equação preditiva específica para determinar o VO2máx de meninos de 10 a 16 anos. Quarenta e dois meninos realizaram teste ergoespirométrico de corrida em esteira rolante com velocidade inicial de 9 km/h até exaustão voluntária. Através da regressão linear múltipla foi possível desenvolver a seguinte equação para a determinação indireta do VO2máx: VO2máx (ml/min) = -1574,06 + (141,38 x Vpico) + (48,34 * Massa corporal), com erro padrão de estimativa = 191,5 ml/min (4,10 ml/kg/min) e o coeficiente de determinação = 0,934. Sugerimos que esta é uma fórmula adequada para predizer o VO2máx para esta população.

The maximal oxygen uptake (VO2max) is the maximal quantity of energy that can be produced by the aerobic metabolism in certain time unity. It can be determined direct or indirectly by predictive equations. The objective of this study was to make a specific predictive equation to determine the VO2max from boys aged 10-16 years-old. Forty-two boys underwent a treadmill running ergospirometric test, with the initial velocity set at 9 km/h, until voluntary exhaustion. By the multiple linear regression was possible to develop the following equation for the indirect determination of the VO2max: VO2max (ml/min) = -1574.06 + (141.38 x Vpeak) + (48.34 * Body mass), with standard error of estimate = 191.5 ml/min (4.10 ml/kg/min) and coefficient of determination = 0.934. We suggest that this formula is appropriate to predict VO2max for this population.

Accuracy of predictive equations for resting energy expenditure (REE) in non-obese and obese Korean children and adolescents

Weight-controlling can be supported by a proper prescription of energy intake. The individual energy requirement is usually determined through resting energy expenditure (REE) and physical activity. Because REE contributes to 60-70% of daily energy expenditure, the assessment of REE is very important. REE is often predicted using various equations, which are usually based on the body weight, height, age, gender, and so on. The aim of this study is to validate the published predictive equations for resting energy expenditure in 76 normal weight and 52 obese Korean children and adolescents in the 7-18 years old age group. The open-circuit indirect calorimetry using a ventilated hood system was used to measure REE. Sixteen REE predictive equations were included, which were based on weight and/or height of children and adolescents, or which were commonly used in clinical settings despite its use based on adults. The accuracy of the equations was evaluated on bias, RMSPE, and percentage of accurate prediction. The means of age and height were not significantly different among the groups. Weight and BMI were significantly higher in obese group (64.0 kg, 25.9 kg/m2) than in the non-obese group (44.8 kg, 19.0 kg/m2). For the obese group, the Molnar, Mifflin, Liu, and Harris-Benedict equations provided the accurate predictions of > 70% (87%, 79% 77%, and 73%, respectively). On the other hand, for non-obese group, only the Molnar equation had a high level of accuracy (bias of 0.6%, RMSPE of 90.4 kcal/d, and accurate prediction of 72%). The accurate prediction of the Schofield (W/WH), WHO (W/WH), and Henry (W/WH) equations was less than 60% for all groups. Our results showed that the Molnar equation appears to be the most accurate and precise for both the non-obese and the obese groups. This equation might be useful for clinical professionals when calculating energy needs in Korean children and adolescents.

Accuracy of Predictive Equations for Resting Metabolic Rates and Daily Energy Expenditures of Police Officials Doing Shift Work by Type of Work

The purpose of this study was to analyze the accuracy of predictive equations for resting metabolic rate (RMR) and daily energy expenditure in policemen on a rotating shift. Subjects were 28 healthy policemen on a rotating shift (males) age of 23-46 years. The participants' RMR was measured by using indirect calorimetry (TrueOne2400) and also calculated from various predicted equations of RMR (Harris-Benedict, Schofield(W)/(WH), FAO/WHO/UNU(W)/(W/H), Cunningham, Mifflin, Liu, Owen, IMNA and Henry(W)/(WH)). The accuracy of these equations were evaluated on basis of accurate prediction (the percentage of subjects whose RMR was predicted within 90% to 110% of the RMR measured), mean difference, root mean squared prediction error, mean % difference, limits of agreement of Bland-Altman method between predicted and measured RMR. The measured RMR value of subjects was 1748 +/- 205.9 kcal. Of the predictive equations tested, the Harris-Benedict equation (mean difference: -14.8 kcal/day, RMSPE: 195.8 kcal/day, mean % difference: 0.1%) was the most accurate and precise, but accuracy in prediction of the equation were only 35.7%. The daily energy expenditure at night-duty was 3062 kcal calculated as multiplying RMR by its physical activity level. Subsequently, daily energy expenditure of day-duty was 2647 kcal and the lowest daily energy expenditure was, 2310 kcal at holiday duty. Daily energy intake of all study participants was 2351 kcal at day-duty, 1959 kcal at night-duty and 1796 kcal at holiday-duty in order. The estimated energy requirements for policemen on a rotating shift on day shift, night shift and holiday came to 2743.6 kcal/day, 2998.6 kcal/day and 2576.9 kcal/day, respectively. These results suggest that estimated energy requirements (EER) of policemen on a rotating shift should be differently proposed by a proper equation which can closely reflect their metabolic status at each time shift.

Identificação do lactato mínimo de corredores adolescentes em teste de pista de três estágios incrementais / Lactate minimum identification in youth runners through a track test of tree incremental stages

OBJETIVO: Analisar a possibilidade de se determinar a velocidade de lactato mínimo (LM) em corredores adolescentes utilizando-se apenas três estágios incrementais. MÉTODOS: Onze indivíduos (13,7 ± 1,0 anos; 47,3 ± 12,1kg; 160,0 ± 1,0cm; 18,3 ± 1,8kg/m²) realizaram três testes de corrida em pista de atletismo em dias distintos: 1) desempenho de 3.000m (Vm3.000); 2) teste de LM que consistiu de um sprint de 500m para indução a hiperlactatemia, seguido de 10min de recuperação e seis séries de 800m em intensidades de 83, 86, 89, 92, 95 e 98 por cento da Vm3.000; 3) teste de LM com três estágios (LMp3) semelhante ao protocolo anterior, porém, com três séries de 800m em intensidades de 83, 89 e 98 por cento da Vm3.000. Durante o primeiro minuto de recuperação entre os estágios dos testes dois e três foram coletadas amostras de sangue para dosagem de lactato sanguíneo. Para determinação do LM foram empregadas: a) inspeção visual (LM) e b) função polinomial de segunda ordem para identificar o LM em seis estágios (LMp) e três estágios (LMp3). RESULTADOS: ANOVA demonstrou não haver diferenças entre as velocidades de lactato mínimo (m.min-1) identificadas pelos diferentes métodos (LM = 221,7 ± 15,4 vs. LMp = 227,1 ± 10,8 vs. LMp3 = 224,1 ± 11,2;). Altas correlações foram observadas entre os protocolos estudados e destes com a Vm3.000 (p < 0,01). CONCLUSÃO: Foi possível identificar a velocidade de corrida correspondente ao LM em adolescentes mesmo utilizando-se de apenas três estágios incrementais (LMp3).

OBJECTIVE: To analyze the possibility of determining the lactate minimum (LM) velocity in prepubertal runners applying only three incremental stages. METHODS: Eleven teens (13.7 ± 1.0 years; 47.3 ± 12.1 kg; 160.0 ± 1.0 cm; 18.3 ± 1.8 kg/m²) performed three run tests on athletic track field on different days: 1) performance at 3000m (Vm3000) 2) LM test consisting of a 500m sprint for hyperlactatemia induction, followed by 10min of recovery and six sets of 800m at intensities of 83, 86, 89, 92, 95 and 98 percent of Vm3000, 3) LM test with threestage (LMp3) similarly to the previous protocol; however, with only three sets of 800m at intensities of 83, 89 and 98 percent of Vm3000. During the first recovery minute between the second and third test stages, blood samples were collected in order to measure blood lactate. The following criteria were used to determine LM: a) visual inspection (LM), b) polynomial function of second order to LM six stages (LMp) and to three stages (LMp3). RESULTS: ANOVA showed no differences between speeds (m.min-1) identified in the studied methods (LM = 221.7 ± 15.4 vs. LMp = 227.1 ± 10.8 vs. LMp3 = 224.1 ± 11.2). High correlations were observed between the studied protocols and between these protocols and the Vm3000 (p <0.01). CONCLUSION: It was possible to identify the velocity corresponding to the LM in youth runners even when applying only three incremental stages for identification of the LM intensity (LMp3).