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Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study.

ABSTRACT: Background:Pneumonia complicated by septic shock is associated with significant morbidity and mortality. Classification and regression tree methodology is an intuitive method for predicting clinical outcomes using binary splits. We aimed to improve the prediction of in-hospital mortality in patients with pneumonia and septic shock using decision tree analysis. Methods:Classification and regression tree models were applied to all patients with pneumonia-associated septic shock in the international, multicenter Cooperative Antimicrobial Therapy of Septic Shock database between 1996 and 2015. The association between clinical factors (time to appropriate antimicrobial therapy, severity of illness) and in-hospital mortality was evaluated. Accuracy in predicting clinical outcomes, sensitivity, specificity, and area under receiver operating curve of the final model was evaluated in training (n?=?2111) and testing datasets (n?=?2111). Results:The study cohort contained 4222 patients, and in-hospital mortality was 51%. The mean time from onset of shock to administration of appropriate antimicrobials was significantly higher for patients who died (17.2 h) compared to those who survived (5.0 h). In the training dataset (n?=?2111), a tree model using Acute Physiology and Chronic Health Evaluation II Score, lactate, age, and time to appropriate antimicrobial therapy yielded accuracy of 73% and area under the receiver operating curve 0.75. The testing dataset (n?=?2111) had accuracy of 69% and area under the receiver operating curve 0.72. Conclusions:Overall mortality (51%) in patients with pneumonia complicated by septic shock is high. Increased time to administration of antimicrobial therapy, Acute Physiology and Chronic Health Evaluation II Score, serum lactate, and age were associated with increased in-hospital mortality. Classification and regression tree methodology offers a simple prognostic model with good performance in predicting in-hospital mortality.

SUBMITTER: Speiser JL

PROVIDER: S-EPMC6186142 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study.

Speiser Jaime L JL Karvellas Constantine J CJ Shumilak Geoffery G Sligl Wendy I WI Mirzanejad Yazdan Y Gurka Dave D Kumar Aseem A Kumar Anand A

Journal of intensive care 20181012

<h4>Background</h4>Pneumonia complicated by septic shock is associated with significant morbidity and mortality. Classification and regression tree methodology is an intuitive method for predicting clinical outcomes using binary splits. We aimed to improve the prediction of in-hospital mortality in patients with pneumonia and septic shock using decision tree analysis.<h4>Methods</h4>Classification and regression tree models were applied to all patients with pneumonia-associated septic shock in t ...[more]

PMID: 30349726

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Project description:OBJECTIVES:Sepsis care is becoming a more common target for hospital performance measurement, but few studies have evaluated the acceptability of sepsis or septic shock mortality as a potential performance measure. In the absence of a gold standard to identify septic shock in claims data, we assessed agreement and stability of hospital mortality performance under different case definitions. DESIGN:Retrospective cohort study. SETTING:U.S. acute care hospitals. PATIENTS:Hospitalized with septic shock at admission, identified by either implicit diagnosis criteria (charges for antibiotics, cultures, and vasopressors) or by explicit International Classification of Diseases, 9th revision, codes. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We used hierarchical logistic regression models to determine hospital risk-standardized mortality rates and hospital performance outliers. We assessed agreement in hospital mortality rankings when septic shock cases were identified by either explicit International Classification of Diseases, 9th revision, codes or implicit diagnosis criteria. Kappa statistics and intraclass correlation coefficients were used to assess agreement in hospital risk-standardized mortality and hospital outlier status, respectively. Fifty-six thousand six-hundred seventy-three patients in 308 hospitals fulfilled at least one case definition for septic shock, whereas 19,136 (33.8%) met both the explicit International Classification of Diseases, 9th revision, and implicit septic shock definition. Hospitals varied widely in risk-standardized septic shock mortality (interquartile range of implicit diagnosis mortality: 25.4-33.5%; International Classification of Diseases, 9th revision, diagnosis: 30.2-38.0%). The median absolute difference in hospital ranking between septic shock cohorts defined by International Classification of Diseases, 9th revision, versus implicit criteria was 37 places (interquartile range, 16-70), with an intraclass correlation coefficient of 0.72, p value of less than 0.001; agreement between case definitions for identification of outlier hospitals was moderate (kappa, 0.44 [95% CI, 0.30-0.58]). CONCLUSIONS:Risk-standardized septic shock mortality rates varied considerably between hospitals, suggesting that septic shock is an important performance target. However, efforts to profile hospital performance were sensitive to septic shock case definitions, suggesting that septic shock mortality is not currently ready for widespread use as a hospital quality measure.

Project description:BackgroundObjective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data.MethodsTwo regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status.ResultsA total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables.ConclusionsWe developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.

Project description:BackgroundIn the typical setting of gene-selection problems from high-dimensional data, e.g., gene expression data from microarray or next-generation sequencing-based technologies, an enormous volume of high-throughput data is generated, and there is often a need for a simple, computationally-inexpensive, non-parametric screening procedure than can quickly and accurately find a low-dimensional variable subset that preserves biological information from the original very high-dimensional data (dimension p > 40,000). This is in contrast to the very sophisticated variable selection methods that are computationally expensive, need pre-processing routines, and often require calibration of priors.ResultsWe present a tree-based sequential CART (S-CART) approach to variable selection in the binary classification setting and compare it against the more sophisticated procedures using simulated and real biological data. In simulated data, we analyze S-CART performance versus (i) a random forest (RF), (ii) a fully-parametric Bayesian stochastic search variable selection (SSVS), and (iii) the moderated t-test statistic from the LIMMA package in R. The simulation study is based on a hierarchical Bayesian model, where dataset dimensionality, percentage of significant variables, and substructure via dependency vary. Selection efficacy is measured through false-discovery and missed-discovery rates. In all scenarios, the S-CART method is seen to consistently outperform SSVS and RF in both speed and detection accuracy. We demonstrate the utility of the S-CART technique both on simulated data and in a control-treatment mouse study. We show that the network analysis based on the S-CART-selected gene subset in essence recapitulates the biological findings of the study using only a fraction of the original set of genes considered in the study's analysis.ConclusionsThe relatively simple-minded gene selection algorithms like S-CART may often in practical circumstances be preferred over much more sophisticated ones. The advantage of the "greedy" selection methods utilized by S-CART and the likes is that they scale well with the problem size and require virtually no tuning or training while remaining efficient in extracting the relevant information from microarray-like datasets containing large number of redundant or irrelevant variables.AvailabilityThe MATLAB 7.4b code for the S-CART implementation is available for download from https://neyman.mcg.edu/posts/scart.zip.

Project description:BACKGROUND:Assessment of disease severity in patients with septic shock (SS) is crucial in determining optimal level of care. In both pre- and in-hospital settings, the clinical picture alone is not sufficient for assessing disease severity and outcomes. Because blood lactate level is included in the clinical criteria of SS it should be considered to improve the assessment of its severity. This study aims to investigate the relationship between pre-hospital blood lactate level and 30-day mortality in patients with SS. METHODS:From 15 April 2017 to 15 April 2019, patients with SS requiring pre-hospital Mobile Intensive Care Unit intervention (MICU) were prospectively included in the LAPHSUS study, an observational, non-randomized controlled study. Pre-hospital blood lactate levels were measured at the time of first contact between the patients and the MICU. RESULTS:Among the 183 patients with septic shock requiring action by the MICU drawn at random from LAPHSUS study patients, six (3%) were lost to follow-up on the 30th day and thus 177 (97%) were analyzed for blood lactate levels (mean age 70 ± 14 years). Pulmonary, urinary and digestive infections were probably the cause of the SS in respectively 58%, 21% and 11% of the cases. The 30-day overall mortality was 32%. Mean pre-hospital lactatemia was significantly different between patients who died and those who survived (respectively 7.1 ± 4.0 mmol/L vs. 5.9 ± 3.5 mmol/L, p < 10-3). Using Cox regression analysis adjusted for potential confounders we showed that a pre-hospital blood lactate level ? 4 mmol/L significantly predicted 30-day mortality in patients with SS (adjusted hazard ratio = 2.37, 95%CI (1.01-5.57), p = 0.04). CONCLUSION:In this study, we showed that pre-hospital lactatemia predicts 30-day mortality in patients with septic shock handled by the MICU. Further studies will be needed to evaluate if pre-hospital lactatemia alone or combined with clinical scores could affect the triage decision-making process for those patients.

Project description:ObjectivesTo assess the variability in short-term sepsis mortality by hospital among Centers for Medicare and Medicaid Services beneficiaries in the United States during 2013-2014.DesignA retrospective cohort design.SettingHospitalizations from 3,068 acute care hospitals that participated in the Centers for Medicare and Medicaid Services inpatient prospective payment system in 2013 and 2014.PatientsMedicare fee-for-service beneficiaries greater than or equal to 65 years old who had an inpatient hospitalization coded with present at admission severe sepsis or septic shock.InterventionsNone.Measurements and main resultsIndividual level mortality was assessed as death at or within 7 days of hospital discharge and aggregated to calculate hospital-level mortality rates. We used a logistic hierarchal linear model to calculate mortality risk-adjusted for patient characteristics. We quantified variability among hospitals using the median odds ratio and calculated risk-standardized mortality rates for each hospital. The overall crude mortality rate was 34.7%. We found significant variability in mortality by hospital (p < 0.001). The middle 50% of hospitals had similar risk-standardized mortality rates (32.7-36.9%), whereas the decile of hospitals with the highest risk-standardized mortality rates had a median mortality rate of 40.7%, compared with a median of 29.2% for hospitals in the decile with the lowest risk-standardized mortality rates. The median odds ratio (1.29) was lower than the adjusted odds ratios for several measures of patient comorbidities and severity of illness, including present at admission organ dysfunction, no identified source of infection, and age.ConclusionsIn a large study of present at admission sepsis among Medicare beneficiaries, we showed that mortality was most strongly associated with underlying comorbidities and measures of illness on arrival. However, after adjusting for patient characteristics, mortality also modestly depended on where a patient with sepsis received care, suggesting that efforts to improve sepsis outcomes in lower performing hospitals could impact sepsis survival.

Dataset Information

Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study.

Publications

Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study.

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