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Related Concept Videos

Dosage Regimen Designs: Nomograms and Tabulations01:23

Dosage Regimen Designs: Nomograms and Tabulations

Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
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Development and Validation of a Nomogram Model to Predict Obstructive Sleep Apnea.

Jingjing Huang1,2,3, Zhujian Wang4, Fang Shi1,2,3

  • 1Department of Otolaryngology-Head and Neck Surgery, Eye & ENT Hospital of Fudan University, Shanghai, China.

Ear, Nose, & Throat Journal
|April 11, 2024
PubMed
Summary

A new nomogram model predicts obstructive sleep apnea (OSA) using blood oxygen and routine blood indicators. This approach offers a faster, cost-effective alternative to polysomnography for OSA diagnosis.

Keywords:
blood cellsnomogram modelrisk factorssleep apnea syndromesleep respiratory disorder

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Area of Science:

  • Medical Diagnostics
  • Respiratory Medicine
  • Biostatistics

Background:

  • Polysomnography (PSG) is a definitive but time-consuming and expensive diagnostic tool for suspected obstructive sleep apnea (OSA).
  • There is a need for more accessible and efficient methods to predict OSA risk.
  • Routine blood indicators and oxygen saturation levels are readily available clinical data.

Purpose of the Study:

  • To develop and validate a nomogram model for predicting OSA.
  • The model primarily utilizes oxygen saturation and routine blood test results.
  • To provide a cost-effective and time-saving alternative for OSA screening.

Main Methods:

  • Retrospective analysis of 685 patients with suspected OSA.
  • Multivariate analysis to identify significant predictors for nomogram construction.
  • Internal validation using bootstrapping and assessment of discrimination and calibration (C-index).

Main Results:

  • Key predictors identified: age, gender, BMI, mean pulse oxygen saturation, % nighttime with SpO2 < 90%, RBC, HCT, and RDW-SD.
  • The nomogram demonstrated strong predictive performance with a C-index of 0.935 (internal validation) and 0.957 (external validation).
  • The model exhibited good calibration and discrimination in both validation cohorts.

Conclusions:

  • The developed nomogram effectively predicts OSA risk using accessible clinical parameters.
  • This tool can aid physicians in clinical decision-making, potentially reducing diagnostic time and costs.
  • The nomogram offers a promising, efficient screening method for patients with suspected OSA.