Joint analysis of duration of ventilation, length of intensive care, and mortality of COVID-19 patients: a multistate approach

Hazard, Derek; Kaier, Klaus; Cube, Maja von; Grodd, Marlon; Bugiera, Lars; Lambert, Jérôme; Wolkewitz, Martin

doi:10.1186/s12874-020-01082-z

Cited by 92 publications

(53 citation statements)

References 15 publications

(11 reference statements)

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“…Recently, both Hazard et al 18 and Schmidt et al 19 proposed using a multistate model similar in spirit to ours, focusing only on patients admitted to the intensive care unit in the most severe of clinical states, requiring extracorporeal membrane oxygenation (ECMO); the cohorts in these studies are small (77 and 83 patients, respectively), and they are relevant only to the subset of COVID-19 patients requiring ECMO treatment. Moreover, these works did not include covariates, while our results demonstrate the importance of baseline and time-dependent covariates in the multistate models (see Supplementary Tables S2 to S4 ).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Roimi

Gutman

Somer

et al. 2021

Journal of the American Medical Informatics Association

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Objective The spread of COVID-19 has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics. Materials and Methods We develop a model of patient clinical course based on an advanced multistate survival model. The model predicts the patient's disease course in terms of clinical states—critical, severe, or moderate. The model also predicts hospital utilization on the level of entire hospitals or healthcare systems. We cross-validated the model using a nationwide registry following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1st to May 2nd, 2020 (n = 2,703). Results Per-day mean absolute errors for predicted total and critical-care hospital-bed utilization were 4.72 ± 1.07 and 1.68 ± 0.40 respectively, over cohorts of 330 hospitalized patients; AUCs for prediction of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of patient influx scenarios on day-by-day healthcare system utilization. We provide an accompanying R software package. Discussion The proposed model accurately predicts total and critical-care hospital utilization. The model enables evaluating impacts of patient influx scenarios on utilization, accounting for the state of currently hospitalized patients and characteristics of incoming patients. We show that accurate hospital-load predictions were possible using only a patient’s age, sex, and day-by-day clinical state (critical, severe or moderate). Conclusion The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Roimi

Gutman

Somer

et al. 2021

Journal of the American Medical Informatics Association

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“…Multistate models provide a reliable analysis of outcomes in severely ill patients and a clear visual presentation of the clinical path of COVID-19 patients [ 13 ]. This modeling approach also permits an understanding of possible influencing factors and in which part of the path they may intervene.…”

Section: Discussionmentioning

confidence: 99%

Multistate Modeling of COVID-19 Patients Using a Large Multicentric Prospective Cohort of Critically Ill Patients

Ursino

Dupuis

Buetti

et al. 2021

JCM

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The mortality of COVID-19 patients in the intensive care unit (ICU) is influenced by their state at admission. We aimed to model COVID-19 acute respiratory distress syndrome state transitions from ICU admission to day 60 outcome and to evaluate possible prognostic factors. We analyzed a prospective French database that includes critically ill COVID-19 patients. A six-state multistate model was built and 17 transitions were analyzed either using a non-parametric approach or a Cox proportional hazard model. Corticosteroids and IL-antagonists (tocilizumab and anakinra) effects were evaluated using G-computation. We included 382 patients in the analysis: 243 patients were admitted to the ICU with non-invasive ventilation, 116 with invasive mechanical ventilation, and 23 with extracorporeal membrane oxygenation. The predicted 60-day mortality was 25.9% (95% CI: 21.8%–30.0%), 44.7% (95% CI: 48.8%–50.6%), and 59.2% (95% CI: 49.4%–69.0%) for a patient admitted in these three states, respectively. Corticosteroids decreased the risk of being invasively ventilated (hazard ratio (HR) 0.59, 95% CI: 0.39–0.90) and IL-antagonists increased the probability of being successfully extubated (HR 1.8, 95% CI: 1.02–3.17). Antiviral drugs did not impact any transition. In conclusion, we observed that the day-60 outcome in COVID-19 patients is highly dependent on the first ventilation state upon ICU admission. Moreover, we illustrated that corticosteroid and IL-antagonists may influence the intubation duration.

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“…The model is shown in S3 Fig . The statistical methodology and required assumptions are outlined in detail in [ 14 ]. The multistate model accounts for the states hospitalisation in a ‘regular ward’, ‘ICU’, ‘MV’, ‘ECMO’ as well as ‘discharge alive’ and ‘death’.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany

et al. 2020

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Background Reported mortality of hospitalised Coronavirus Disease-2019 (COVID-19) patients varies substantially, particularly in critically ill patients. So far COVID-19 in-hospital mortality and modes of death under state of the art care have not been systematically studied. Methods This retrospective observational monocenter cohort study was performed after implementation of a non-restricted, dynamic tertiary care model at the University Medical Center Freiburg, an experienced acute respiratory distress syndrome (ARDS) and extracorporeal membrane-oxygenation (ECMO) referral center. All hospitalised patients with PCR-confirmed SARS-CoV-2 infection were included. The primary endpoint was in-hospital mortality, secondary endpoints included major complications and modes of death. A multistate analysis and a Cox regression analysis for competing risk models were performed. Modes of death were determined by two independent reviewers. Results Between February 25, and May 8, 213 patients were included in the analysis. The median age was 65 years, 129 patients (61%) were male. 70 patients (33%) were admitted to the intensive care unit (ICU), of which 57 patients (81%) received mechanical ventilation and 23 patients (33%) ECMO support. Using multistate methodology, the estimated probability to die within 90 days after COVID-19 onset was 24% in the whole cohort. If the levels of care at time of study entry were accounted for, the probabilities to die were 16% if the patient was initially on a regular ward, 47% if in the intensive care unit (ICU) and 57% if mechanical ventilation was required at study entry. Age ≥65 years and male sex were predictors for in-hospital death. Predominant complications–as judged by two independent reviewers–determining modes of death were multi-organ failure, septic shock and thromboembolic and hemorrhagic complications. Conclusion In a dynamic care model COVID-19-related in-hospital mortality remained very high. In the absence of potent antiviral agents, strategies to alleviate or prevent the identified complications should be investigated. In this context, multistate analyses enable comparison of models-of-care and treatment strategies and allow estimation and allocation of health care resources.

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Joint analysis of duration of ventilation, length of intensive care, and mortality of COVID-19 patients: a multistate approach

Cited by 92 publications

References 15 publications

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Multistate Modeling of COVID-19 Patients Using a Large Multicentric Prospective Cohort of Critically Ill Patients

COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany

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