MétaCan
Menu
Back to cohort

The Effects of Postacute Rehabilitation on Mortality, Chronic Care Dependency, Health Care Use, and Costs in Sepsis Survivors

2022· article· en· W4306411854 on OpenAlexaff
Daniel T. Winkler, Norman Rose, Antje Freytag, Wolfgang Sauter, Melissa Spoden, Anna Schettler, Lisa Wedekind, Josephine Storch, Bianka Ditscheid, Peter Schlattmann, Konrad Reinhart, Christian Günster, Christiane S. Hartog, Carolin Fleischmann-Struzek

Bibliographic record

VenueAnnals of the American Thoracic Society · 2022
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineRehabilitationPropensity score matchingSepsisCohortObservational studyPhysical therapySubgroup analysisEmergency medicineIntensive care medicineInternal medicineMeta-analysis

Abstract

fetched live from OpenAlex

Abstract Rationale Sepsis often leads to long-term functional deficits and increased mortality in survivors. Postacute rehabilitation can decrease long-term sepsis mortality, but its impact on nursing care dependency, health care use, and costs is insufficiently understood. Objectives To assess the short-term (7–12 months postdischarge) and long-term (13–36 months postdischarge) effect of inpatient rehabilitation within 6 months after hospitalization on mortality, nursing care dependency, health care use, and costs. Methods An observational cohort study used health claims data from the health insurer AOK (Allgemeine Ortskrankenkasse). Among 23.0 million AOK beneficiaries, adult beneficiaries hospitalized with sepsis in 2013–2014 were identified by explicit codes from the International Classification of Diseases, Tenth Revision. The study included patients who were nonemployed presepsis, for whom rehabilitation is reimbursed by the AOK and thus included in the dataset, and who survived at least 6 months postdischarge. The effect of rehabilitation was estimated by statistical comparisons of patients with rehabilitation (treatment group) and those without (reference group). Possible differential effects were investigated for the subgroup of ICU-treated sepsis survivors. The study used inverse probability of treatment weighting based on propensity scores to adjust for differences in relevant covariates. Costs for rehabilitation in the 6 months postsepsis were not included in the cost analysis. Results Among 41,918 6-month sepsis survivors, 17.2% (n = 7,224) received rehabilitation. There was no significant difference in short-term survival between survivors with and without rehabilitation. Long-term survival rates were significantly higher in the rehabilitation group (90.4% vs. 88.7%; odds ratio [OR] = 1.2; 95% confidence interval [95% CI] = 1.1–1.3; P = 0.003). Survivors with rehabilitation had a higher mean number of hospital readmissions (7–12 months after sepsis: 0.82 vs. 0.76; P = 0.014) and were more frequently dependent on nursing care (7–12 months after sepsis: 47.8% vs. 42.3%; OR = 1.2; 95% CI = 1.2–1.3; P < 0.001; 13–36 months after sepsis: 52.5% vs. 47.5%; OR = 1.2; 95% CI = 1.1–1.3; P < 0.001) compared with those without rehabilitation, whereas total health care costs at 7–36 months after sepsis did not differ between groups. ICU-treated sepsis patients with rehabilitation had higher short- and long-term survival rates (short-term: 93.5% vs. 90.9%; OR = 1.5; 95% CI = 1.2–1.7; P < 0.001; long-term: 89.1% vs. 86.3%; OR = 1.3; 95% CI = 1.1–1.5; P < 0.001) than ICU-treated sepsis patients without rehabilitation. Conclusions Rehabilitation within the first 6 months after ICU- and non–ICU-treated sepsis is associated with increased long-term survival within 3 years after sepsis without added total health care costs. Future work should aim to confirm and explain these exploratory findings.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.078
GPT teacher head0.434
Teacher spread0.356 · 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".

Quick stats

Citations16
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the American Thoracic SocietySame topicSepsis Diagnosis and TreatmentFrench-language works237,207