scholarly journals Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Comparative Machine Learning Study (Preprint)

2020 ◽  
Author(s):  
Sujeong Hur ◽  
Ji Young Min ◽  
Junsang Yoo ◽  
Kyunga Kim ◽  
Chi Ryang Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Patient Safety ◽  
Intensive Care ◽  
Predictive Value ◽  
Prediction Models ◽  
Support Vector ◽  
Unplanned Extubation ◽  
Electronic Health Record Data ◽  
Icu Patients

BACKGROUND Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered as the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. OBJECTIVE This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. METHODS This study was conducted an academic tertiary hospital in Seoul. The hospital had approximately 2,000 inpatient beds and 120 intensive care unit (ICU) beds. The number of patients, on daily basis, was approximately 9,000 for the out-patient. The number of annual ICU admission was approximately 10,000. We conducted a retrospective study between January 1, 2010 and December 31, 2018. A total of 6,914 extubation cases were included. We developed an unplanned extubation prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used area under the receiver operator characteristic curve (AUROC). Sensitivity, specificity, positive predictive value negative predictive value, and F1-score were also determined for each model. For performance evaluation, we also used calibration curve, the Brier score, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS Among the 6,914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was more likely to occur during the night shift compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality was higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.762, and for SVM was 0.740. CONCLUSIONS We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787, which was obtained using RF. CLINICALTRIAL N/A

scholarly journals Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1866 ◽  
Author(s):  
Liao ◽  
Wang ◽  
Zhang ◽  
Abbod ◽  
Shih ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Pseudomonas Aeruginosa ◽  
Intensive Care ◽  
Prediction Models ◽  
Support Vector ◽  
Ventilator Associated Pneumonia ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pseudomonas Aeruginosa Infection

One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients’ data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models’ performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 ± 0.0135 and 0.8686 ± 0.0422 accuracies, 0.9714 ± 0.0131, 0.9250 ± 0.0423 sensitivities, and 0.9288 ± 0.0306, 0.8639 ± 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 ± 0.0058 and 0.9410 ± 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.

scholarly journals Performance of a machine-learning algorithm to predict hypotension in mechanically ventilated patients with COVID-19 admitted to the intensive care unit: a cohort study

2021 ◽  
Author(s):  
Ward H. van der Ven ◽  
Lotte E. Terwindt ◽  
Nurseda Risvanoglu ◽  
Evy L. K. Ie ◽  
Marije Wijnberge ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Time To Event ◽  
Arterial Waveform ◽  
Icu Patients ◽  
Mechanically Ventilated

AbstractThe Hypotension Prediction Index (HPI) is a commercially available machine-learning algorithm that provides warnings for impending hypotension, based on real-time arterial waveform analysis. The HPI was developed with arterial waveform data of surgical and intensive care unit (ICU) patients, but has never been externally validated in the latter group. In this study, we evaluated diagnostic ability of the HPI with invasively collected arterial blood pressure data in 41 patients with COVID-19 admitted to the ICU for mechanical ventilation. Predictive ability was evaluated at HPI thresholds from 0 to 100, at incremental intervals of 5. After exceeding the studied threshold, the next 20 min were screened for positive (mean arterial pressure (MAP) < 65 mmHg for at least 1 min) or negative (absence of MAP < 65 mmHg for at least 1 min) events. Subsequently, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and time to event were determined for every threshold. Almost all patients (93%) experienced at least one hypotensive event. Median number of events was 21 [7–54] and time spent in hypotension was 114 min [20–303]. The optimal threshold was 90, with a sensitivity of 0.91 (95% confidence interval 0.81–0.98), specificity of 0.87 (0.81–0.92), PPV of 0.69 (0.61–0.77), NPV of 0.99 (0.97–1.00), and median time to event of 3.93 min (3.72–4.15). Discrimination ability of the HPI was excellent, with an area under the curve of 0.95 (0.93–0.97). This validation study shows that the HPI correctly predicts hypotension in mechanically ventilated COVID-19 patients in the ICU, and provides a basis for future studies to assess whether hypotension can be reduced in ICU patients using this algorithm.

Association of opioid exposure during intensive care unit stays with post-discharge opioid use: A retrospective study and literature review

2021 ◽  
Vol 17 (6) ◽  
pp. 511-516
Author(s):  
Yoonsun Mo, MS, PharmD, BCPS, BCCCP ◽  
John Zeibeq, MD ◽  
Nabil Mesiha, MD ◽  
Abou Bakar, PharmD ◽  
Maram Sarsour, PharmD ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Hospital Discharge ◽  
Invasive Mechanical Ventilation ◽  
High Dose ◽  
Opioid Use ◽  
Electronic Health Record Data ◽  
Icu Patients ◽  
Mechanically Ventilated

Objective: To evaluate whether pain management strategies within intensive care unit (ICU) settings contribute to chronic opioid use upon hospital discharge in opioid-naive patients requiring invasive mechanical ventilation. Design: A retrospective, observational study.Setting: An 18-bed mixed ICU at a community teaching hospital located in Brooklyn, New York.Participants: This study included mechanically ventilated patients requiring continuous opioid infusion from April 25, 2017 to May 16, 2019. Patients were excluded if they received chronic opioid therapy at home or expired during this hospital admission. Eligible patients were identified using an electronic health record data query.Main outcome measure(s): The proportion of ICU patients who continued to require opioids upon ICU and hospital discharge. Results: A total of 196 ICU patients were included in this study. Of these, 22 patients were transferred to a regular floor while receiving a fentanyl transdermal patch. However, the fentanyl patch treatment was continued only for three patients (2 percent) at hospital discharge.Conclusions: This retrospective study suggested that high-dose use of opioids in mechanically ventilated, opioid-naive ICU patients was not associated with continued opioid use upon hospital discharge.

Abstract 15875: Prediction of Intensive Care Unit Admission Among Patients Hospitalized for COVID-19: The Intermountain Coronavirus ICU Risk Model (CORONA-ICU)

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Heidi T May ◽  
Joseph B Muhlestein ◽  
Benjamin D Horne ◽  
Kirk U Knowlton ◽  
Tami L Bair ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Beta Blockers ◽  
Risk Scores ◽  
Channel Blockers ◽  
Icu Patients

Background: Treatment for COVID-19 has created surges in hospitalizations, intensive care unit (ICU) admissions, and the need for advanced medical therapy and equipment, including ventilators. Identifying patients early on who are at risk for more intensive hospital resource use and poor outcomes could result in shorter hospital stays, lower costs, and improved outcomes. Therefore, we created clinical risk scores (CORONA-ICU and -ICU+) to predict ICU admission among patients hospitalized for COVID-19. Methods: Intermountain Healthcare patients who tested positive for SARS-CoV-2 and were hospitalized between March 4, 2020 and June 8, 2020 were studied. Derivation of CORONA-ICU risk score models used weightings of commonly collected risk factors and medicines. The primary outcome was admission to the ICU during hospitalization, and secondary outcomes included death and ventilator use. Results: A total of 451 patients were hospitalized for a SARS-CoV-2 positive infection, and 191 (42.4%) required admission to the ICU. Patients admitted to the ICU were older (58.2 vs. 53.6 years), more often male (61.3% vs. 48.5%), and had higher rates of hyperlipidemia, hypertension, diabetes, and peripheral arterial disease. ICU patients more often took ACE inhibitors, beta-blockers, calcium channel blockers, diuretics, and statins. Table 1 shows variables that were evaluated and included in the CORONA-ICU risk prediction models. Models adding medications (CORONA-ICU+) improved risk-prediction. Though not created to predict death and ventilator use, these models did so with high accuracy (Table 2). Conclusion: The CORONA-ICU and -ICU+ models, composed of commonly collected risk factors without or with medications, were shown to be highly predictive of ICU admissions, death, and ventilator use. These models can be efficiently derived and effectively identify high-risk patients who require more careful observation and increased use of advanced medical therapies.

scholarly journals Detection of Aspergillus fumigatus in Blood Samples from Critically Ill Patients in Intensive Care Units by Use of the SeptiFast Assay: TABLE 1

2016 ◽  
Vol 54 (7) ◽  
pp. 1918-1921 ◽  
Author(s):  
Joerg Steinmann ◽  
Jan Buer ◽  
Peter-Michael Rath
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Positive Predictive Value ◽  
Aspergillus Fumigatus ◽  
Critically Ill ◽  
Predictive Value ◽  
Icu Patients ◽  
Blood Samples ◽  
Content Type ◽  
Whole Blood Samples

We retrospectively analyzed the performance and relevance of the SeptiFast assay in detectingAspergillus fumigatusDNA in whole blood samples from 38 critically ill intensive care unit (ICU) patients with probable or proven invasive aspergillosis (IA) and 100 ICU patients without IA. The assay exhibited 66% sensitivity, 98% specificity, a 93% positive predictive value, and an 88% negative predictive value.A. fumigatusDNAemia was associated with poor outcome.

scholarly journals Renal dysfunction decreases specificity and overturns cutoff value of serum CC16 while prohibiting 7-day mortality prediction in diagnosing acute respiratory distress syndrome in intensive care unit.

2020 ◽  
Author(s):  
Jinle Lin ◽  
Wuyuan Tao ◽  
Jian Wei ◽  
Wu Jian ◽  
Wenwu Zhang ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Renal Dysfunction ◽  
Predictive Value ◽  
Distress Syndrome ◽  
Icu Patients ◽  
Cutoff Value

Abstract Background: A contradictory tendency between occurrence of acute respiratory distress syndrome (ARDS) and serum club cell protein 16 (CC16) level, However, renal dysfunction (RD) separately raised serum CC16 in our current observation. The purpose of this study was to find the limitation caused by renal dysfunction in the diagnostic performance of CC16 on ARDS in intensive care unit (ICU) patients. Method: We measured serum CC16 in 479 ICU patients. Patients were divided into six subgroups: control, acute kidney injury (AKI), chronic kidney dysfunction (CKD), ARDS, ARDS+AKI, and ARDS+CKD. The cutoff value, sensitivity and specificity of serum CC16 were assessed by receiver operating characteristic curves. Result: Serum CC16 increased among the ARDS group when compared to the control group, which helps identify ARDS and predicts the outcome in patients with normal renal function. However, level of serum CC16 was similar among ARDS+AKI, ARDS+CKD, AIK and CKD groups. Consequently, when compare to AKI and CKD, specificity for diagnosing whether ARDS or ARDS with renal failure decreased from 86.62% to 2.82% or 81.70% to 2.12%. Consistently, a cutoff value of 11.57 ng/mL was overturned from previously at 32.77 ng/mL or 33.72 ng/mL. Moreover, its predictive value for mortality is prohibited before 7 day but works after 28 day. Conclusion: Renal dysfunction limits the specificity, cutoff point, and predictive value at 7-day mortality of CC16 in diagnosing ARDS among ICU patients.

scholarly journals Severity and Outcome Detection for the Cancer Patients with COVID-19 Using Machine Learning Models

2021 ◽  
Author(s):  
Akbar Davoodi ◽  
Shaghayegh Haghjooy Javanmard ◽  
Golnaz Vaseghi ◽  
Amirreza Manteghinejad
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Cancer Center ◽  
Support Vector ◽  
Neutrophil Lymphocyte Ratio ◽  
Icu Admission ◽  
Reactive Protein ◽  
Risk Of Death

Abstract Background:The COVID-19 pandemic challenges the healthcare system to provide enough resources to battle the pandemic without jeopardizing routine treatments. As a result, this is important that we can predict the outcomes of patients at the time of admission. This study aims to apply different machine learning (ML) models for predicting Intensive Care Unit (ICU) admission and mortality of Cancer Patients infected with COVID-19.Methods:This study's data were collected from a referral cancer center in Iran. The study included all patients with cancer and a confirmed diagnosis of COVID-19.Different ML prediction algorithms like Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbours (kNN), Random Forest (RF), and Support Vector Machine (SVM) were used. Also, we applied the SelectKBest method to find the most important features for predicting ICU admission and mortality.Results:Three hundred thirty-nine patients enrolled in the study. One hundred fifteen were admitted to the Intensive Care Unit (ICU), and 118 patients died during the hospital admission. The Area Under Curve (AUC) for predicting mortality is 0.61 for LR, 0.74 for NB, 0.61 for kNN, 0.6 for SVM, and 0.79 for RF. The AUC for predicting ICU admission is 0.61 for LR, 0.74 for NB, 0.56 for kNN, 0.55 for SVM, and 0.7 for RF.C-reactive protein (CRP), Aspartate transaminase (AST), and Neutrophil-Lymphocyte Ratio (NLR) also are the most common features in predicting ICU admission and mortality.Conclusion:Our findings show the promise of different AI methods for predicting the risk of death or ICU in cancer patients infected with COVID-19, highlighting the importance of first laboratory results and patients' symptoms.

Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit

2018 ◽  
Vol 33 (8) ◽  
pp. 546-553 ◽  
Author(s):  
Iván Sánchez Fernández ◽  
Arnold J. Sansevere ◽  
Marina Gaínza-Lein ◽  
Kush Kapur ◽  
Tobias Loddenkemper
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Explanatory Models ◽  
Critically Ill Children ◽  
Support Vector ◽  
Discriminative Performance ◽  
Number Of Patients

The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine learning algorithms for predicting mortality in a database of 414 critically ill children undergoing cEEG in the ICU. The area under the receiver operating characteristic curve (AUC) in the test subset was highest for stepwise selection/elimination models (AUC = 0.82) followed by least absolute shrinkage and selection operator (LASSO) and support vector machine with linear kernel (AUC = 0.79), and random forest (AUC = 0.71). The explanatory models had the poorest discriminative performance (AUC = 0.63 for the model without considering etiology and AUC = 0.45 for the model considering etiology). Using few variables and a relatively small number of patients, machine learning techniques added information to explanatory models for prediction of in-hospital mortality.

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