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IMPORTANCE: Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates. OBJECTIVE: The study aimed to compare how different model metrics rank the importance of various covariates. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available. METHODS: This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most optimal was chosen. The machine-learning model was used to rank model covariates based on gain, cover, and frequency to identify risk factors for sleep disorder and feature importance was evaluated using both univariable and multivariable t-statistics. A correlation matrix was created to determine the similarity of the importance of variables ranked by different model metrics. RESULTS: The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), Positive Predictive Value of 0.806 (SD = 0.025), and Negative Predictive Value of 0.737 (SD = 0.034). The model metrics from the machine learning of gain and cover were strongly positively correlated with one another (r > 0.70). Model metrics from the multivariable model and univariable model were weakly negatively correlated with machine learning model metrics (R between -0.3 and 0). CONCLUSION: The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models.
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Aprendizaje Automático , Trastornos del Inicio y del Mantenimiento del Sueño , Humanos , Trastornos del Inicio y del Mantenimiento del Sueño/epidemiología , Masculino , Femenino , Persona de Mediana Edad , Estudios Transversales , Adulto , Estudios Retrospectivos , Factores de Riesgo , Encuestas Nutricionales , Modelos Logísticos , Anciano , Modelos EstadísticosRESUMEN
OBJECTIVE AND AIMS: Identification of associations between the obese category of weight in the general US population will continue to advance our understanding of the condition and allow clinicians, providers, communities, families, and individuals make more informed decisions. This study aims to improve the prediction of the obese category of weight and investigate its relationships with factors, ultimately contributing to healthier lifestyle choices and timely management of obesity. METHODS: Questionnaires that included demographic, dietary, exercise and health information from the US National Health and Nutrition Examination Survey (NHANES 2017-2020) were utilized with BMI 30 or higher defined as obesity. A machine learning model, XGBoost predicted the obese category of weight and Shapely Additive Explanations (SHAP) visualized the various covariates and their feature importance. Model statistics including Area under the receiver operator curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value and feature properties such as gain, cover, and frequency were measured. SHAP explanations were created for transparent and interpretable analysis. RESULTS: There were 6,146 adults (age > 18) that were included in the study with average age 58.39 (SD = 12.94) and 3122 (51%) females. The machine learning model had an Area under the receiver operator curve of 0.8295. The top four covariates include waist circumference (gain = 0.185), GGT (gain = 0.101), platelet count (gain = 0.059), AST (gain = 0.057), weight (gain = 0.049), HDL cholesterol (gain = 0.032), and ferritin (gain = 0.034). CONCLUSION: In conclusion, the utilization of machine learning models proves to be highly effective in accurately predicting the obese category of weight. By considering various factors such as demographic information, laboratory results, physical examination findings, and lifestyle factors, these models successfully identify crucial risk factors associated with the obese category of weight.
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Algoritmos , Aprendizaje Automático , Obesidad , Humanos , Femenino , Masculino , Persona de Mediana Edad , Adulto , Estados Unidos/epidemiología , Inteligencia Artificial , Anciano , Encuestas Nutricionales , Índice de Masa Corporal , Curva ROC , Peso CorporalRESUMEN
Social determinants of health (SDOH) are important predictors of poor clinical outcomes in chronic diseases, but their associations among the general cirrhosis population and liver transplantation (LT) are limited. We conducted a retrospective, multiinstitutional analysis of adult (≥18-years-old) patients with cirrhosis in metropolitan Chicago to determine the associations of poor neighborhood-level SDOH on decompensation complications, mortality, and LT waitlisting. Area deprivation index and covariates extracted from the American Census Survey were aspects of SDOH that were investigated. Among 15 101 patients with cirrhosis, the mean age was 57.2 years; 6414 (42.5%) were women, 6589 (43.6%) were non-Hispanic White, 3652 (24.2%) were non-Hispanic Black, and 2662 (17.6%) were Hispanic. Each quintile increase in area deprivation was associated with poor outcomes in decompensation (sHR [subdistribution hazard ratio] 1.07; 95% CI 1.05-1.10; P < .001), waitlisting (sHR 0.72; 95% CI 0.67-0.76; P < .001), and all-cause mortality (sHR 1.09; 95% CI 1.06-1.12; P < .001). Domains of SDOH associated with a lower likelihood of waitlisting and survival included low income, low education, poor household conditions, and social support (P < .001). Overall, patients with cirrhosis residing in poor neighborhood-level SDOH had higher decompensation, and mortality, and were less likely to be waitlisted for LT. Further exploration of structural barriers toward LT or optimizing health outcomes is warranted.
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Cirrosis Hepática , Trasplante de Hígado , Determinantes Sociales de la Salud , Listas de Espera , Humanos , Trasplante de Hígado/mortalidad , Femenino , Masculino , Persona de Mediana Edad , Listas de Espera/mortalidad , Estudios Retrospectivos , Cirrosis Hepática/cirugía , Cirrosis Hepática/mortalidad , Pronóstico , Tasa de Supervivencia , Estudios de Seguimiento , Chicago/epidemiología , Factores de Riesgo , Adulto , Anciano , Factores Socioeconómicos , Características de la ResidenciaRESUMEN
Importance: The prevalence of obesity among United States adults has increased from 34.9% in 2013-2014 to 42.8% in 2017-2018. Developing methods to model the increase of obesity over-time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency. Methods: A cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort. Results: Of the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30). Conclusion: United States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long-term weight status.
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Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives. This report utilized a publicly available machine-learning data package in R (MLDataR) and computational packages (XGBoost) to highlight techniques for machine-learning model development and visualization with SHaply Additive exPlanations (SHAP). A simple six-step process along with example code was constructed to build and visualize machine-learning models. A concrete set of three steps was developed to help with interpretation. Further teaching of these methods could benefit researchers by providing alternative methods for data analysis in medical studies. These could help researchers without computational experience to get a feel for machine learning to better understand the literature and technique.
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IMPORTANCE: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. METHODS: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. RESULTS: Of the 6146 patients that met the inclusion criteria, 3024 (49%) of patients were male and 3122 (51%) of patients were female. There were 2252 (37%) White patients, 1257 (20%) Hispanic patients, 1636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2411 (39%) patients lost weight, and 3735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2058 (33%) were normal weight (18.5 ≤ BMI < 25), 1376 (22%) were overweight (25 ≤ BMI < 30) and 2625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. CONCLUSIONS: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.
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Sobrepeso , Delgadez , Adulto , Humanos , Masculino , Femenino , Estados Unidos , Sobrepeso/epidemiología , Encuestas Nutricionales , Estudios Retrospectivos , Delgadez/epidemiología , Estudios Transversales , Cadenas de Markov , Índice de Masa Corporal , Obesidad/epidemiología , Aumento de PesoRESUMEN
BACKGROUND: Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions. OBJECTIVE: The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults. METHODS: A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. RESULTS: 7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP-Hip Circumference (cm) (Gain = 7.2%), BMXHT-standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%). CONCLUSION: Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks.
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Asma , Adulto , Humanos , Estudios Transversales , Encuestas Nutricionales , Asma/diagnóstico , Asma/epidemiología , Aprendizaje Automático , Estudios de CohortesRESUMEN
Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.
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Hipertensión , Masculino , Humanos , Femenino , Adulto , Hipertensión/diagnóstico , Hipertensión/epidemiología , Encuestas Nutricionales , Estudios Transversales , Reproducibilidad de los Resultados , Aprendizaje AutomáticoRESUMEN
Background: Depression affects personal and public well-being and identification of natural therapeutics such as nutrition is necessary to help alleviate this public health concern. Objective: The study aimed to identify feature importance in a machine learning model using solely nutrition covariates. Methods: A retrospective analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). Depressive symptoms were evaluated using the validated 9-item Patient Health Questionnaire (PHQ-9), and all adult patients (total of 7929 individuals) who completed the PHQ-9 and total nutritional intake questionnaire were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. Results: 7929 patients met the inclusion criteria in this study. The machine learning model had 24 out of a total of 60 features that were found to be significant on univariate analysis (p < 0.01 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.603, Sensitivity = 0.943, Specificity = 0.163. The top four highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Potassium Intake (Gain = 6.8%), Vitamin E Intake (Gain = 5.7%), Number of Foods and Beverages Reported (Gain = 5.7%), and Vitamin K Intake (Gain 5.6%). Conclusion: Machine learning models with feature importance can be utilized to identify nutritional covariates for further study in patients with clinical symptoms of depression.
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Background and aims: Depression is a major public health concern that affects over 4% of the global population. Identification of new nonpharmacologic recommendations will help decrease the burden of disease. The overarching of this study was to examine the association between physical activity and depressive symptoms in a large sample of adults in the United States. Methods: Presently, researchers utilized data from the National Health and Nutrition Examination Surveys (NHANES 2017-2020), which is a retrospective, complex, multistage, representative, and modern cohort of the United States. Adult patients ( > 18 years; N = 8091) with complete 9-item Patient Health Questionnaire (PHQ-9) information were included in the study. The PHQ-9 is a well-validated survey, per literature, scores ≥10 are considered to have clinically relevant depression. Univariable and multivariable logistic regression was fit for active and sedentary activities on clinical depression (PHQ-9 ≥ 10). The acquisition and analysis of the data within this study were approved by the National Center for Health Statistics Ethics Review Board. Results: After adjusting for potential confounders like age, race, sex, and income, we found that increased vigorous exercise was associated with lower rates of depressive symptoms. Each extra day of vigorous exercise was associated with 11% decreased odds of depression (odd ratio [OR]: 0.89, confidence interval [CI]: 0.83-0.96, p < 0.01). Increased sedentary activity was associated with increased depression. Each extra hour per day of sedentary activity was associated with a 6% increase in odds of depression (OR: 1.06, (1.02-1.10, p < 0.01). Conclusion: To conclude, exercise appears to be protective against depressive symptoms; however, further prospective studies are required to ascertain whether exercise causes decreased depressive symptoms.
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Background: There has been evidence to suggest associations between vitamins and lung function. Objective: This study aimed to examine the association between vitamin B6 and spirometry values. Methods: A cross-sectional study was done using National Health and Nutritional Examination Surveys (NHANES) 2007-2012, which is a nationally representative, modern cohort. Spirometry, a clinical pulmonary function test, measured the amount and speed of air a person could exhale after taking the deepest possible breath after forceful expiratory volume at 1 s (FEV1) and forced vital capacity (FVC). After determination of the relationship of the linearity of variables, univariable and multivariable models were fitted to investigate the effect of vitamin B6 on FEV1 and FVC. The National Center for Health Statistics Ethics Review Board granted permission for the study's data collection and analysis. Results: Of 19,160 individuals who had complete information on vitamin B6 intake, FEV1, and FVC, it was found each mg of vitamin B6 intake was associated with increase in 166.41 mL of FEV1 (95% CI: 156.71, 176.12; P < 0.01) and 221.6 mL of FVC (95% CI: 209.62, 233.57; P < 0.01). After controlling for potential confounders (age, race, sex, body mass index, education, and income), multiple linear regression found that each mg of vitamin B6 was associated with increase in 25.98 mL of FEV1 (95% CI: 19.15, 32.80, P < 0.01) and 38.97 mL of FVC (95% CI: 30.65, 47.30, P < 0.01). Conclusion: Increased vitamin B6 intake is associated with improvement in lung function. Further prospective studies are required to ascertain whether increased vitamin B6 can lead to increased long-term spirometry measurements and the specific therapeutic dose-response relationship.
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BACKGROUND: There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality. OBJECTIVE: The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency. METHODS: Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map. RESULTS: Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics. CONCLUSION: Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.
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Bicarbonatos , Insuficiencia Cardíaca , Anciano , Femenino , Humanos , Masculino , Cuidados Críticos , Unidades de Cuidados Intensivos , Estudios Retrospectivos , Persona de Mediana Edad , Anciano de 80 o más AñosRESUMEN
Background and Aim: The COVID-19 disease course can be thought of as a function of prior risk factors consisting of comorbidities and outcomes. Survival analysis data for diabetic patients with COVID-19 from an up to date and representative sample can increase efficiency in resource allocation. The study aimed to quantify mortality in Mexico for individuals with diabetes in the setting of COVID-19 hospitalization. Methods: This retrospective cohort study utilized publicly available data from the Mexican Federal Government, covering the period from April 14, 2020, to December 20, 2020 (last accessed). Survival analysis techniques were applied, including Kaplan-Meier curves to estimate survival probabilities, log-rank tests to compare survival between groups, Cox proportional hazard models to assess the association between diabetes and mortality risk, and restricted mean survival time (RMST) analyses to measure the average survival time. Results: A total of 402,388 adults age greater than 18 with COVID-19 were used in the analysis. Mean age = 16.16 (SD = 15.55), 214,161 males (53%). Twenty-day Kaplan-Meier estimates of mortality were 32% for COVID-19 patients with diabetes and 10.2% for those without diabetes with log-rank p < 0.01. Univariable analysis showed increased mortality in diabetic patients (hazard ratio [HR]: 3.61, 95% confidence interval [CI]: 3.54-3.67, p < 0.01) showing a 254% increase in death. After controlling for confounding variables, multivariate analysis continued to show increased mortality in diabetics (HR: 1.37, 95% CI: 1.29-1.44, p < 0.01) indicating a 37% increase in death. Multivariable RMST at Day 20 showed in Mexico, hospitalized COVID-19 patients were associated with less mean survival time by 2.01 days (p < 0.01) and a 10% increased mortality (p < 0.01). Conclusions: In the present analysis, COVID-19 patients with diabetes in Mexico had shorter survival times. Further interventions aimed at improving comorbidities in the population, particularly in individuals with diabetes, may contribute to better outcomes in COVID-19 patients.
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Background: Depression is a rapidly increasing public health concern, affecting >4% of the global population. Identification of new nutritional recommendations is needed to help combat this increasing public health concern. Objectives: The study aimed to examine the association between vitamin E intake and depressive symptoms. Methods: A retrospective study was conducted by using a nationally representative, modern cohort (NHANES 2017-2020). Depressive symptoms were assessed through the validated 9-item Patient Health Questionnaire (PHQ-9). All adult patients ([≥18 y old], 8091 total adults) who answered the PHQ-9 and daily nutritional values questionnaires were selected for this study. Per literature, patients with PHQ-9 scores ≥10 were considered to have depressive symptoms. Univariable and multivariable logistic regressions were used to investigate the effect of vitamin E on depressive symptoms as ascertained by PHQ-9. The acquisition and analysis of the data within this study was approved by the NCHS ethics review board. Results: After controlling for potential confounders (age, race, sex, and income), we observed that increased vitamin E (up until 15 mg/d) was associated with decreased rates of depressive symptoms, with each 5 mg increase in vitamin E associated with 13% decreased odds of symptoms of depression (OR: 0.87; 95% CI: 0.77, 0.97; P < 0.01). Additional intake above 15 mg/d, the daily recommended amount by the Food and Nutrition Board, did not change the odds of depression (OR: 1.05; 95% CI: 0.92, 1.16; P = 0.44). Conclusions: Increased vitamin E intake (up to 15 mg/d) is associated with decreased depressive symptoms. Further prospective studies are required to ascertain whether increased vitamin E can protect against depressive symptoms and the specific therapeutic dose-response relationship.
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Background and Aims: All fields have seen an increase in machine-learning techniques. To accurately evaluate the efficacy of novel modeling methods, it is necessary to conduct a critical evaluation of the utilized model metrics, such as sensitivity, specificity, and area under the receiver operator characteristic curve (AUROC). For commonly used model metrics, we proposed the use of analytically derived distributions (ADDs) and compared it with simulation-based approaches. Methods: A retrospective cohort study was conducted using the England National Health Services Heart Disease Prediction Cohort. Four machine learning models (XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boost) were used. The distribution of the model metrics and covariate gain statistics were empirically derived using boot-strap simulation (N = 10,000). The ADDs were created from analytic formulas from the covariates to describe the distribution of the model metrics and compared with those of bootstrap simulation. Results: XGBoost had the most optimal model having the highest AUROC and the highest aggregate score considering six other model metrics. Based on the Anderson-Darling test, the distribution of the model metrics created from bootstrap did not significantly deviate from a normal distribution. The variance created from the ADD led to smaller SDs than those derived from bootstrap simulation, whereas the rest of the distribution remained not statistically significantly different. Conclusions: ADD allows for cross study comparison of model metrics, which is usually done with bootstrapping that rely on simulations, which cannot be replicated by the reader.
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Background: Diabetes mellitus is a chronic health condition that has been linked with an increased risk of severe illness and mortality from COVID-19. In Mexico, the impact of diabetes on COVID-19 outcomes in hospitalized patients has not been fully quantified. Understanding the increased risk posed by diabetes in this patient population can help healthcare providers better allocate resources and improve patient outcomes. Objective: The objective of this study was to quantify the extent outcomes (pneumonia, intensive care unit [ICU] stay, intubation, and death) are worsened in diabetic patients with COVID-19. Methods: Between April 14, 2020 and December 20, 2020 (last accessed), data from the open-source COVID-19 database maintained by the Mexican Federal Government were examined. Utilizing hospitalized COVID-19 patients with complete outcome data, a retrospective cohort study (N = 402,388) was carried out. In relation to COVID-19, both univariate and multivariate logistic regression were used to investigate the effect of diabetes on specific outcomes. Results: The analysis included 402,388 adults (age >18) with confirmed hospitalized COVID-19 cases with mean age 46.16 (standard deviation = 15.55), 214,161 (53%) male. The outcomes delineated included pneumonia (N = 88,064; 22%), ICU requirement (N = 23,670; 6%), intubation (N = 23,670; 6%), and death (N = 55,356; 14%). After controlling for confounding variables diabetes continued to be an independent risk factor for both pneumonia (odds ratio [OR]: 1.8, confidence interval [CI]: 1.76-1.84, p < 0.01), ICU requirement (OR: 1.09, CI: 1.04-1.14, p < 0.01), intubation (OR: 1.07, CI: 1.04-1.11, p < 0.01), and death (OR: 1.88, CI: 1.84-1.93, p < 0.01) in COVID-19 patients. Conclusions: According to the study, all outcomes (pneumonia, ICU requirement, intubation, and death) were greater among hospitalized individuals with diabetes and COVID-19. Additional study is required to acquire a better understanding of how diabetes affects COVID-19 outcomes and to develop practical mitigation techniques for the risk of severe sickness and complications in this particular patient population.
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Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.
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Enfermedad de la Arteria Coronaria , Humanos , Masculino , Femenino , Persona de Mediana Edad , Enfermedad de la Arteria Coronaria/diagnóstico , Enfermedad de la Arteria Coronaria/epidemiología , Encuestas Nutricionales , Estudios Retrospectivos , Estudios Transversales , Factores de Riesgo , Aprendizaje AutomáticoRESUMEN
IMPORTANCE: Sleep is critical to a person's physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. OBJECTIVE: The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. DESIGN, SETTING, AND PARTICIPANTS: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. METHODS: A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. RESULTS: Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). CONCLUSION: Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.
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Trastornos del Inicio y del Mantenimiento del Sueño , Humanos , Masculino , Femenino , Persona de Mediana Edad , Trastornos del Inicio y del Mantenimiento del Sueño/epidemiología , Encuestas Nutricionales , Estudios Retrospectivos , Estudios Transversales , Factores de Riesgo , Aprendizaje AutomáticoRESUMEN
Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.
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Algoritmos , Redes Neurales de la Computación , Humanos , Reproducibilidad de los Resultados , Simulación por Computador , Aprendizaje AutomáticoRESUMEN
This technical report serves as a comprehensive guide for conducting a phenome-wide association study (PheWAS) utilizing data extracted from the Nationwide Inpatient Sample 2020. Specifically tailored to individuals diagnosed with pancreatic cysts and lung cancer, the report establishes a step-by-step workflow designed to assist researchers in uncovering potential associations within this specific cohort. The methodology outlined in the report ensures clarity and reproducibility by employing a curated cohort sourced from the GitHub repository and executed using R for robust data analysis. The code encompasses pivotal steps, including the utilization of a QQ plot as a crucial diagnostic tool aimed at identifying systematic biases or associations. Additionally, the report incorporates the creation of a Manhattan plot, delving into essential mathematical considerations to enhance the interpretability of the results. Notably, the report elucidates the handling of the International Classification of Disease version 10 (ICD-10) codes, providing a sample approach for their segmentation to analyze associations by diagnostic categories. The segmentation aligns with the guidelines outlined in the American Medical Association's ICD-10-CM 2022, the Complete Official Codebook with Guidelines (American Medical Association Press, 2021), ensuring a standardized and rigorous analytical process. This comprehensive guide equips researchers with the tools and insights needed to navigate the complexities of PheWAS within the context of pancreatic cysts and lung cancer, fostering transparency, reproducibility, and meaningful scientific exploration.