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Takhar A, Surda P, Ahmad I, Amin N, Arora A, Camporota L, Denniston P, El-Boghdadly K, Kvassay M, Macekova D, Munk M, Ranford D, Rabcan J, Tornari C, Wyncoll D, Zaitseva E, Hart N, Tricklebank S. Timing of Tracheostomy for Prolonged Respiratory Wean in Critically Ill Coronavirus Disease 2019 Patients: A Machine Learning Approach. Crit Care Explor 2020; 2:e0279. [PMID: 33225305 PMCID: PMC7673767 DOI: 10.1097/cce.0000000000000279] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To propose the optimal timing to consider tracheostomy insertion for weaning of mechanically ventilated patients recovering from coronavirus disease 2019 pneumonia. We investigated the relationship between duration of mechanical ventilation prior to tracheostomy insertion and in-hospital mortality. In addition, we present a machine learning approach to facilitate decision-making. DESIGN Prospective cohort study. SETTING Guy's & St Thomas' Hospital, London, United Kingdom. PATIENTS Consecutive patients admitted with acute respiratory failure secondary to coronavirus disease 2019 requiring mechanical ventilation between March 3, 2020, and May 5, 2020. INTERVENTIONS Baseline characteristics and temporal trends in markers of disease severity were prospectively recorded. Tracheostomy was performed for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. Decision tree was constructed using C4.5 algorithm, and its classification performance has been evaluated by a leave-one-out cross-validation technique. MEASUREMENTS AND MAIN RESULTS One-hundred seventy-six patients required mechanical ventilation for acute respiratory failure, of which 87 patients (49.4%) underwent tracheostomy. We identified that optimal timing for tracheostomy insertion is between day 13 and day 17. Presence of fibrosis on CT scan (odds ratio, 13.26; 95% CI [3.61-48.91]; p ≤ 0.0001) and Pao2:Fio2 ratio (odds ratio, 0.98; 95% CI [0.95-0.99]; p = 0.008) were independently associated with tracheostomy insertion. Cox multiple regression analysis showed that chronic obstructive pulmonary disease (hazard ratio, 6.56; 95% CI [1.04-41.59]; p = 0.046), ischemic heart disease (hazard ratio, 4.62; 95% CI [1.19-17.87]; p = 0.027), positive end-expiratory pressure (hazard ratio, 1.26; 95% CI [1.02-1.57]; p = 0.034), Pao2:Fio2 ratio (hazard ratio, 0.98; 95% CI [0.97-0.99]; p = 0.003), and C-reactive protein (hazard ratio, 1.01; 95% CI [1-1.01]; p = 0.005) were independent late predictors of in-hospital mortality. CONCLUSIONS We propose that the optimal window for consideration of tracheostomy for ventilatory weaning is between day 13 and 17. Late predictors of mortality may serve as adverse factors when considering tracheostomy, and our decision tree provides a degree of decision support for clinicians.
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Affiliation(s)
- Arunjit Takhar
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Pavol Surda
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Imran Ahmad
- Department of Anaesthesia, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
- Kings College London, United Kingdom
| | - Nikul Amin
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Asit Arora
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Luigi Camporota
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Poppy Denniston
- Department of Respiratory Medicine, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Kariem El-Boghdadly
- Department of Anaesthesia, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
- Kings College London, United Kingdom
| | - Miroslav Kvassay
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia
| | - Denisa Macekova
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia
| | - Michal Munk
- Department of Informatics, Constantine the Philosopher University, Nitra, Slovakia
| | - David Ranford
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Jan Rabcan
- Department of Informatics, Faculty of Management Science and Informatics, University of Zilina, Zilina, Slovakia
| | - Chysostomos Tornari
- Department of Otolaryngology and Head and Neck Surgery, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Duncan Wyncoll
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Elena Zaitseva
- Department of Informatics, Constantine the Philosopher University, Nitra, Slovakia
| | - Nicholas Hart
- Lane Fox Respiratory Unit, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
- Kings College London, United Kingdom
| | - Stephen Tricklebank
- Department of Critical Care, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
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Missing data techniques in classification for cardiovascular dysautonomias diagnosis. Med Biol Eng Comput 2020; 58:2863-2878. [PMID: 32970269 DOI: 10.1007/s11517-020-02266-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 09/08/2020] [Indexed: 10/23/2022]
Abstract
Missing data (MD) is a common and inevitable problem facing data mining (DM)-based decision systems in e-health since many medical historical datasets contain a huge number of missing values. Therefore, a pre-processing stage is usually required to deal with missing values before building any DM-based decision system. The purpose of this paper is to evaluate the impact of MD techniques on classification systems in cardiovascular dysautonomias diagnosis. We analyzed and compared the accuracy rates of four classification techniques: random forest (RF), support vector machines (SVM), C4.5 decision tree, and Naive Bayes (NB), using two MD techniques: deletion or imputation with k-nearest neighbors (KNN). A total of 216 experiments were therefore carried out using three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random and NMAR: not missing at random), two MD techniques (deletion and KNN imputation), nine MD percentages from 10 to 90% over a dataset collected from the autonomic nervous system (ANS) unit of the University Hospital Avicenne in Morocco. The results obtained suggest that using KNN imputation rather than deletion enhances the accuracy rates of the four classifiers. Moreover, the MD percentages have a negative impact on the performance of classification techniques regardless of the MD mechanisms and MD techniques used. In fact, the accuracy rates of the four classifiers decrease as the MD percentage increases. Graphical abstract.
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Kadi I, Idri A, Fernandez-Aleman JL. Systematic mapping study of data mining–based empirical studies in cardiology. Health Informatics J 2017; 25:741-770. [DOI: 10.1177/1460458217717636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.
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Affiliation(s)
| | - Ali Idri
- Mohammed V University in Rabat, Morocco
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