Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. METHODS: Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. RESULTS: 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. CONCLUSIONS: The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients.

Sepsis, a dysregulated host response to infection, is a major health burden in terms of both mortality and cost. The difficulties clinicians face in diagnosing sepsis, alongside the insufficiencies of diagnostic biomarkers, motivate the present study. This work develops a machine-learning-based sepsis diagnostic for a high-risk patient group, using a geographically and institutionally diverse collection of nearly 500,000 patient health records. Using only a minimal set of clinical variables, our diagnostics outperform common severity scoring systems and sepsis biomarkers and benefit from being available immediately upon ordering.

Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data.

BACKGROUND: A major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified. OBJECTIVE: In this study, we evaluate a machine learning algorithm for early detection and prediction of AKI. DESIGN: We used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data taken from more than 300 000 inpatient encounters. SETTING: Data were collected from inpatient wards at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center. PATIENTS: Patients older than the age of 18 whose hospital stays lasted between 5 and 1000 hours and who had at least one documented measurement of heart rate, respiratory rate, temperature, serum creatinine (SCr), and Glasgow Coma Scale (GCS). MEASUREMENTS: We tested the algorithm's ability to detect AKI at onset and to predict AKI 12, 24, 48, and 72 hours before onset. METHODS: We tested AKI detection and prediction using the National Health Service (NHS) England AKI Algorithm as a gold standard. We additionally tested the algorithm's ability to detect AKI as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We compared the algorithm's 3-fold cross-validation performance to the Sequential Organ Failure Assessment (SOFA) score for AKI identification in terms of area under the receiver operating characteristic (AUROC). RESULTS: The algorithm demonstrated high AUROC for detecting and predicting NHS-defined AKI at all tested time points. The algorithm achieves AUROC of 0.872 (95% confidence interval [CI], 0.867-0.878) for AKI detection at time of onset. For prediction 12 hours before onset, the algorithm achieves an AUROC of 0.800 (95% CI, 0.792-0.809). For 24-hour predictions, the algorithm achieves AUROC of 0.795 (95% CI, 0.785-0.804). For 48-hour and 72-hour predictions, the algorithm achieves AUROC values of 0.761 (95% CI, 0.753-0.768) and 0.728 (95% CI, 0.719-0.737), respectively. LIMITATIONS: Because of the retrospective nature of this study, we cannot draw any conclusions about the impact the algorithm's predictions will have on patient outcomes in a clinical setting. CONCLUSIONS: The results of these experiments suggest that a machine learning-based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.

CONTEXTE: Une des principales difficultés liées au traitement de l'insuffisance rénale aiguë (IRA) est le fait que les critères cliniques diagnostiques sont des marqueurs d'une lésion ou d'une dysfonction rénale déjà établie. Il est souhaitable d'intervenir avant une telle issue. En dépistant les patients à risque d'IRA ou atteints d'IRA débutante, les cliniciens seraient en mesure d'intervenir précocement et ainsi prévenir les lésions rénales permanentes. OBJECTIF DE L'ÉTUDE: L'étude visait à évaluer un algorithme d'apprentissage automatique destiné à la prédiction des cas d'IRA et à sa détection précoce. TYPE D'ÉTUDE: Nous avons employé une technique d'apprentissage automatique, soit des ensembles d'arbres décisionnels amplifiés, pour entrainer un outil de prédiction de l'IRA à partir de données rétrospectives provenant de plus de 300 000 consultations auprès de patients hospitalisés. CADRE DE L'ÉTUDE: Les données ont été colligées à partir des dossiers des unités d'hospitalisation du centre médical de l'université Stanford et de l'unité des soins intensifs du centre médical Beth Israel Deaconess. PARTICIPANTS: Ont été inclus dans l'étude tous les patients adultes dont l'hospitalisation avait duré de 5 à 1 000 heures et pour lesquels on disposait d'au moins une mesure parmi les suivantes : pouls, rythme respiratoire, température corporelle, taux de créatinine sérique (SCr) et score de Glasgow. MESURES: Nous avons testé l'efficacité de l'algorithme à détecter l'IRA dès son apparition, et à la prédire 12, 24, 48 et 72 heures avant qu'elle ne se manifeste. MÉTHODOLOGIE: L'algorithme du NHS England a servi de référence pour tester l'efficacité de notre algorithme de prédiction et de détection de l'IRA. Nous avons également testé l'efficacité de notre algorithme à détecter l'IRA telle que définie par les Recommandations de Bonnes Pratiques Cliniques du KDIGO (Kidney Disease: Improving Global Outcomes). Nous avons utilisé la surface sous la courbe ROC (Receiver Operating Characteristic) pour comparer le score SOFA à l'efficacité de validation croisée tripartite de notre algorithme. RÉSULTATS: L'algorithme a démontré une SSROC (surface sous la courbe ROC) élevée pour la détection et la prédiction de l'IRA (telle que définie par le NHS) pour tous les moments testés. En détection de la maladie à son apparition, l'algorithme a obtenu une SSROC de 0,872 (IC 95 % : 0,867-0,878). En prédiction, l'algorithme a obtenu une SSROC de 0,800 (IC 95 % : 0,792-0,809) à 12 heures, de 0,795 à 24 heures (IC 95 % : 0,785-0,804), de 0,761 (IC 95 % : 0,753-0,768) à 48 heures et de 0,728 (IC 95 % : 0,719-0,737) à 72 heures avant l'apparition des premiers symptômes. LIMITES DE L'ÉTUDE: La nature rétrospective de l'étude ne nous permet pas de tirer de conclusions sur les conséquences qu'auront les prédictions de l'algorithme sur les résultats cliniques des patients. CONCLUSION: Les résultats de nos essais laissent supposer qu'un outil de prédiction de l'IRA fondé sur l'apprentissage automatique pourrait offrir d'importantes fonctions pronostiques pour détecter les patients susceptibles de développer une IRA en vue d'une intervention précoce.