Dataset Information

Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes.

ABSTRACT:

Background

Postoperative respiratory complications (PRCs) are associated with significant morbidity, mortality, and hospital costs. Obstructive sleep apnea (OSA), often undiagnosed in the surgical population, may be a contributing factor. Thus, we aimed to develop and validate a score for preoperative prediction of OSA (SPOSA) based on data available in electronic medical records preoperatively.

Methods

OSA was defined as the occurrence of an OSA diagnostic code preceded by a polysomnography procedure. A priori defined variables were analyzed by multivariable logistic regression analysis to develop our score. Score validity was assessed by investigating the score's ability to predict non-invasive ventilation. We then assessed the effect of high OSA risk, as defined by SPOSA, on PRCs within seven postoperative days and in-hospital mortality.

Results

A total of 108,781 surgical patients at Partners HealthCare hospitals (2007-2014) were studied. Predictors of OSA included BMI >25 kg*m^-2 and comorbidities, including pulmonary hypertension, hypertension, and diabetes. The score yielded an area under the curve of 0.82. Non-invasive ventilation was significantly associated with high OSA risk (OR 1.44, 95% CI 1.22-1.69). Using a dichotomized endpoint, 26,968 (24.8%) patients were identified as high risk for OSA and 7.9% of these patients experienced PRCs. OSA risk was significantly associated with PRCs (OR 1.30, 95% CI 1.19-1.43).

Conclusion

SPOSA identifies patients at high risk for OSA using electronic medical record-derived data. High risk of OSA is associated with the occurrence of PRCs.

SUBMITTER: Shin CH

PROVIDER: S-EPMC5450400 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes.

Shin Christina H CH Grabitz Stephanie D SD Timm Fanny P FP Mueller Noomi N Chhangani Khushi K Ladha Karim K Devine Scott S Kurth Tobias T Eikermann Matthias M

BMC anesthesiology 20170530 1

<h4>Background</h4>Postoperative respiratory complications (PRCs) are associated with significant morbidity, mortality, and hospital costs. Obstructive sleep apnea (OSA), often undiagnosed in the surgical population, may be a contributing factor. Thus, we aimed to develop and validate a score for preoperative prediction of OSA (SPOSA) based on data available in electronic medical records preoperatively.<h4>Methods</h4>OSA was defined as the occurrence of an OSA diagnostic code preceded by a poly ...[more]

PMID: 28558716

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Project description:To validate that the symptomless Multi-Variable Apnea Prediction index (sMVAP) is associated with Obstructive Sleep Apnea (OSA) diagnosis and assess the relationship between sMVAP and adverse outcomes in patients having elective surgery. We also compare associations between Bariatric surgery, where preoperative screening for OSA risk is mandatory, and non-Bariatric surgery groups who are not screened routinely for OSA. Using data from 40 432 elective inpatient surgeries, we used logistic regression to determine the relationship between sMVAP and previous OSA, current hypertension, and postoperative complications: extended length of stay (ELOS), intensive-care-unit-stay (ICU-stay), and respiratory complications (pulmonary embolism, acute respiratory distress syndrome, and/or aspiration pneumonia). Higher sMVAP was associated with increased likelihood of previous OSA, hypertension and all postoperative complications (p < .0001). The top sMVAP quintile had increased odds of postoperative complications compared to the bottom quintile. For ELOS, ICU-stay, and respiratory complications, respective odds ratios (95% CI) were: 1.83 (1.62, 2.07), 1.44 (1.32, 1.58), and 1.85 (1.37, 2.49). Compared against age-, gender- and BMI-matched patients having Bariatric surgery, sMVAP was more strongly associated with postoperative complications in non-Bariatric surgical groups, including: (1) ELOS (Orthopedics [p < .0001], Gastrointestinal [p = .024], Neurosurgery [p = .016], Spine [p = .016]); (2) ICU-stay (Orthopedics [p = .0004], Gastrointestinal [p < .0001], and Otorhinolaryngology [p = .0102]); and (3) respiratory complications (Orthopedics [p =.037] and Otorhinolaryngology [p =.011]). OSA risk measured by sMVAP correlates with higher risk for select postoperative complications. Associations are stronger for non-Bariatric surgeries, where preoperative screening for OSA is not routinely performed. Thus, preoperative screening may reduce OSA-related risk for adverse postoperative outcomes.

Project description:BackgroundThe American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.ObjectiveThis study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.MethodsA total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).ResultsA total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).ConclusionsDefined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Project description:Obstructive sleep apnea (OSA) is a common disorder characterized by recurrent episodes of upper airway obstruction during sleep resulting in oxygen desaturation and sleep fragmentation, and associated with increased risk of adverse health outcomes. Metabolites are being increasingly used for biomarker discovery and evaluation of disease processes and progression. Studying metabolomic associations with OSA in a diverse community-based cohort may provide insights into the pathophysiology of OSA. We aimed to develop and replicate a metabolite index for OSA and identify individual metabolites associated with OSA. We studied 219 metabolites and their associations with the apnea hypopnea index (AHI) and with moderate-severe OSA (AHI ≥ 15) in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (n = 3507) using two methods: (1) association analysis of individual metabolites, and (2) least absolute shrinkage and selection operator (LASSO) regression to identify a subset of metabolites jointly associated with OSA, which was used to develop a metabolite index for OSA. Results were validated in the Multi-Ethnic Study of Atherosclerosis (MESA) (n = 475). When assessing the associations with individual metabolites, we identified seven metabolites significantly positively associated with OSA in HCHS/SOL (FDR p < 0.05), of which four associations-glutamate, oleoyl-linoleoyl-glycerol (18:1/18:2), linoleoyl-linoleoyl-glycerol (18:2/18:2) and phenylalanine, were replicated in MESA (one sided-p < 0.05). The OSA metabolite index, composed of 14 metabolites, was associated with a 50% increased risk for moderate-severe OSA (OR = 1.50 [95% CI 1.21-1.85] per 1 SD of OSA metabolite index, p < 0.001) in HCHS/SOL and 55% increased risk (OR = 1.55 [95% CI 1.10-2.20] per 1 SD of OSA metabolite index, p = 0.013) in MESA, both adjusted for demographics, lifestyle, and comorbidities. Similar albeit less significant associations were observed for AHI. Replication of the metabolite index in an independent multi-ethnic dataset demonstrates the robustness of metabolomic-based OSA index to population heterogeneity. Replicated metabolite associations may provide insights into OSA-related molecular and metabolic mechanisms.

Project description:BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society.

Project description:IntroductionDiagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists.MethodWe used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT.ResultsFrom the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, SD = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm.ConclusionTonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist.

Dataset Information

Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes.

Background

Methods

Results

Conclusion

Publications

Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes.

Similar Datasets

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