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Predictors for ICU Admission and Clinical Outcomes of Malignant Hematology Patients Admitted to Critical Care Units

2016· article· en· W2617118699 on OpenAlexaff
Daniel Jacobson, Nicholas Chiu, Matthew C. Cheung, Robert Fowler, Rena Buckstein

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineIntensive care unitComorbidityRetrospective cohort studyEmergency medicineIntensive careInternal medicineIntensive care medicine

Abstract

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Abstract Introduction:The prognosis of patients with hematologic malignancies (HM) admitted to intensive care units (ICU) is historically poor due to complications of treatment and disease progression with reported overall mortality rates of 24.3% to 84.1%. There is little known regarding predictive variables for ICU admission in adult patients with HM. Objective: Our primary objective was to audit the clinical outcomes including mortality of our HM patients admitted to any hospital ICU unit and compare their characteristics and outcomes with non-ICU hospitalized patients. A secondary objective was to identify the predictive factors for ICU admission and survival. Methods: In this single centre retrospective study, we audited 656/2141 consecutive patients with HM who were admitted to our hospital from 2009-2015 and compared the disease, patient characteristics, and clinical outcomes of HM patients who did or did not get admitted to any ICU. We excluded patients admitted for palliative care and in instances where the patient was admitted more than once, we included only the most recent admission. We enriched for patients who were admitted to the ICU over this 6-year period to improve the statistical power of comparing these two populations. The variables considered included: reason for admission, underlying diagnosis, the modified Charlson Comorbidity Index (CCI), selected laboratory parameters at admission to hospital, previous chemotherapy type, line of treatment, timing and intent, body mass index (BMI), GSCF use, age and sex. We also screened for electronic documentation of advanced directives preceding ICU or hospital admission. We compared the characteristics of ICU (n=179, 27%) and non ICU (n=477, 73%) admitted patients using the Fisher exact test and Wilcoxon rank-sum non parametric test for categorical and continuous variables, respectively. To search for the significant predictive factors for ICU admissions and mortality, univariate and multivariate logistic regression analyses were used. Results: Over the 6-year period, the admission rate to any ICU for HM patients was 9.4%. Median age of the 656 patients was 65.0 (IQR 55-74) and 57% were male with a median time from last chemotherapy and diagnosis of 1 month (IQR 0-3) and 7.8 months (IQR 2.3-44), respectively. Selected patient and disease characteristics comparing ICU and non- ICU admitted patients are in Table 1. There were no differences in median age, BMI, gender, time from last chemotherapy, reasons for admission, WBC, and line of chemotherapy. Patients admitted to any ICU were more likely to have received chemotherapy with curative intent (p=0.0323) and have myeloid cancers (p= 0.0008). They had shorter times from diagnosis, lower hemoglobin, platelet and albumin levels but higher creatinine levels, lactate dehydrogenase (LDH) and liver enzymes. ICU admitted patients had higher CCI scores and in particular history of cardio/cerebro-vascular disease. They also had lower rates of advanced directives (32% vs 49%, p=0.0001). 50% of ICU patients were mechanically ventilated and 25% received vasopressors. Median length of hospital stay was 19 days for ICU and 6 days for non-ICU patients and median ICU length of stay was 3 days (IQR 2-6 days). 34 patients (19.0%) died in ICU and but in hospital mortality was 33% (compared with 8% for non ICU). In the multivariate analysis, six covariates significantly related to ICU admission: chemotherapy intent (curative vs. palliative), history of myocardial infarction, lower platelets, creatinine and albumin levels and no advanced directives (Table 2). Median overall survival was 4.7 months vs. not yet reached for ICU and non ICU admitted patients respectively. 30-day and 6-month survivals were 67% and 47% (ICU) compared with 89%, and 77% in non-ICU admitted patients. The predictors of death in ICU admitted patients were male sex (OR 2.6, 95% CI 1-6.7, p =0.03) and mechanical ventilation (OR 6.55, 95% CI 2.6-18.5, p=0.0001). Conclusions:We validate the previously reported high rates of ICU mortality for patients with hematologic malignancies and have identified the risk factors for ICU admission. Patients without advanced directives have a high likelihood for admission to ICU and potentially represent a targetable group for interventions to avoid inappropriately aggressive care. A predictive score is in development to help identify the patients warranting closer scrutiny and goals of care discussions. Disclosures Buckstein: Celgene: Honoraria, Research Funding; Novartis: Honoraria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.442
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations13
Published2016
Admission routes1
Has abstractyes

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