Hope, hype and harms of Big Data

Scott, Ian A

doi:10.1111/imj.14172

Cited by 14 publications

(5 citation statements)

References 29 publications

(43 reference statements)

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“…The limitations of big data analysis (such as missing or duplicate data, poor coding, among others) render large data sets inherently inaccurate. 10 , 14 , 31 Although our study validation was restricted to a retrospective chart review, our overall sensitivity was estimated at 86%. This value is consistent with error rates reported in other studies with large databases, suggesting that these results may closely represent the Portuguese standard of care for PHFs.…”

Section: Discussionmentioning

confidence: 99%

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Proximal humerus fractures: epidemiology and trends in surgical management of hospital-admitted patients in Portugal

Silva¹,

Linhares²,

Leite³

et al. 2022

JSES International

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Section: Discussionmentioning

confidence: 99%

“…This value is consistent with error rates reported in other studies with large databases, suggesting that these results may closely represent the Portuguese standard of care for PHFs. 10 , 14 , 31 …”

Section: Discussionmentioning

confidence: 99%

Proximal humerus fractures: epidemiology and trends in surgical management of hospital-admitted patients in Portugal

Silva¹,

Linhares²,

Leite³

et al. 2022

JSES International

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“…Large data sets are well equipped to longitudinally identify disease patterns, risk factors and associations while allowing for inferences on subgroup patient populations. 49 , 50 There may be an association between Dupuytren contracture and trigger finger disease, both in shared pathways of inflammatory physiology and potential precipitating or causative effects.…”

Section: Discussionmentioning

confidence: 99%

Analysis of a National Database Investigating Development of Trigger Finger after Treatment of Dupuytren Disease

Gehring¹,

Constantine²,

Le³

et al. 2023

Plastic and Reconstructive Surgery - Global Open

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Background: Dupuytren disease is associated with inflammation and myofibroblast overgrowth, as is stenosing tenosynovitis (trigger finger). Both are linked with fibroblast proliferation, but a potential associative link between the diseases is unknown. The purpose of this study was to evaluate the progression of trigger finger following treatment for Dupuytren contracture in a large database. Methods: A commercial database encompassing 53 million patients was utilized from January 1, 2010 to March 31, 2020. The study cohort included patients diagnosed with either Dupuytren disease or trigger finger utilizing International Classification Codes 9 and 10. Terminology codes were used to identify common Dupuytren procedures, as well as trigger finger release. Logistic regression analysis was used to define independent risk factors for developing trigger finger. Results: A total of 593,606 patients were diagnosed with trigger finger. Of these patients, 15,416 (2.6%) were diagnosed with trigger finger after diagnosis of Dupuytren disease, whereas 2603 (0.4%) patients were diagnosed with trigger finger after treatment of Dupuytren contracture. Independent risk factors for trigger finger included age 65 years or older (OR 1.00, P < 0.05), diabetes (OR 1.12, P < 0.05) and obesity (OR 1.20, P < 0.005). Patients who received collagenase clostridium histolyticum treatment (OR 0.34, P < 0.005) for Dupuytren contracture were significantly less likely to develop trigger finger. Conclusions: Dupuytren contracture is associated with inflammation and subsequent trigger finger development at a higher rate than the background population frequency. Collagenase clostridium histolyticum injection may lead to a decreased risk of trigger finger requiring surgical intervention in patients with risk factors.

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“…To date, although 3 report accuracy [ 32 , 33 , 37 ] superior to that of our ensemble approach, these models were restricted to ICU data sets from the United States and China and are, therefore, not generalizable to the general medical and surgical wards of hospitals where UFH is most frequently administered. Furthermore, compared with all existing studies of ML in UFH dosing, ours was the only one, apart from one small external validation in a hemodialysis setting [ 31 , 66 ], to evaluate model performance when applied to new unseen data. External validation is considered an essential step before assessing the efficacy in controlled clinical trials and subsequent implementation in routine practice [ 65 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

Abdel-Hafez¹,

Scott²,

Falconer³

et al. 2022

Interact J Med Res

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Background Unfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range. Thus, a more efficient approach is required. Objective In this study, we aimed to develop and validate a machine learning (ML) algorithm to predict aPTT within 12 hours after a specified bolus and maintenance dose of UFH. Methods This was a retrospective cohort study of 3019 patient episodes of care from January 2017 to August 2020 using data collected from electronic health records of 5 hospitals in Queensland, Australia. Data from 4 hospitals were used to build and test ensemble models using cross-validation, whereas data from the fifth hospital were used for external validation. We built 2 ML models: a regression model to predict the aPTT value after a UFH bolus dose and a multiclass model to predict the aPTT, classified as subtherapeutic (aPTT <70 seconds), therapeutic (aPTT 70-100 seconds), or supratherapeutic (aPTT >100 seconds). Modeling was performed using Driverless AI (H2O), an automated ML tool, and 17 different experiments were iteratively conducted to optimize model accuracy. Results In predicting aPTT, the best performing model was an ensemble with 4x LightGBM models with a root mean square error of 31.35 (SD 1.37). In predicting the aPTT class using a repurposed data set, the best performing ensemble model achieved an accuracy of 0.599 (SD 0.0289) and an area under the receiver operating characteristic curve of 0.735. External validation yielded similar results: root mean square error of 30.52 (SD 1.29) for the aPTT prediction model, and accuracy of 0.568 (SD 0.0315) and area under the receiver operating characteristic curve of 0.724 for the aPTT multiclassification model. Conclusions To the best of our knowledge, this is the first ML model applied to intravenous UFH dosing that has been developed and externally validated in a multisite adult general medical and surgical inpatient setting. We present the processes of data collection, preparation, and feature engineering for replication.

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Hope, hype and harms of Big Data

Cited by 14 publications

References 29 publications

Proximal humerus fractures: epidemiology and trends in surgical management of hospital-admitted patients in Portugal

Proximal humerus fractures: epidemiology and trends in surgical management of hospital-admitted patients in Portugal

Analysis of a National Database Investigating Development of Trigger Finger after Treatment of Dupuytren Disease

Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach

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