Evidence map›Paper›PMID 36536376›Full record

Observational studyBMC health services research2022

The effect of time-varying capacity utilization on 14-day in-hospital mortality: a retrospective longitudinal study in Swiss general hospitals.

Narayan Sharma, Giusi Moffa, René Schwendimann, Olga Endrich, Dietmar Ausserhofer, Michael Simon

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.4field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Narayan SharmaDepartment Public Health (DPH), Institute of Nursing Science (INS), University of Basel, Bernoullistrasse 28, 4056, Basel, Switzerland.ORCID https://orcid.org/0000-0001-8698-5469
Giusi MoffaDepartment of Mathematics and Computer Science, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-2739-0454
René SchwendimannDepartment Public Health (DPH), Institute of Nursing Science (INS), University of Basel, Bernoullistrasse 28, 4056, Basel, Switzerland.ORCID https://orcid.org/0000-0003-4804-1656
Olga EndrichDirectorate of Medicine, Inselspital University Hospital Bern, Bern, Switzerland.ORCID https://orcid.org/0000-0003-0839-9821
Dietmar AusserhoferDepartment Public Health (DPH), Institute of Nursing Science (INS), University of Basel, Bernoullistrasse 28, 4056, Basel, Switzerland.ORCID https://orcid.org/0000-0002-0319-4766
Michael SimonDepartment Public Health (DPH), Institute of Nursing Science (INS), University of Basel, Bernoullistrasse 28, 4056, Basel, Switzerland. m.simon@unibas.ch.ORCID https://orcid.org/0000-0003-2349-7219
University of Basel · CHUniversity Hospital of Bern · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh bed-occupancy (capacity utilization) rates are commonly thought to increase in-hospital mortality; however, little evidence supports a causal relationship between the two. This observational study aimed to assess three time-varying covariates-capacity utilization, patient turnover and clinical complexity level- and to estimate causal effect of time-varying high capacity utilization on 14 day in-hospital mortality.

methodsThis retrospective population-based analysis was based on routine administrative data (n = 1,152,506 inpatient cases) of 102 Swiss general hospitals. Considering the longitudinal nature of the problem from available literature and expert knowledge, we represented the underlying data generating mechanism as a directed acyclic graph. To adjust for patient turnover and patient clinical complexity levels as time-varying confounders, we fitted a marginal structure model (MSM) that used inverse probability of treatment weights (IPTWs) for high and low capacity utilization. We also adjusted for patient age and sex, weekdays-vs-weekend, comorbidity weight, and hospital type.

resultsFor each participating hospital, our analyses evaluated the ≥85th percentile as a threshold for high capacity utilization for the higher risk of mortality. The mean bed-occupancy threshold was 83.1% (SD 8.6) across hospitals and ranged from 42.1 to 95.9% between hospitals. For each additional day of exposure to high capacity utilization, our MSM incorporating IPTWs showed a 2% increase in the odds of 14-day in-hospital mortality (OR 1.02, 95% CI: 1.01 to 1.03).

conclusionsExposure to high capacity utilization increases the mortality risk of inpatients. Accurate monitoring of capacity utilization and flexible human resource planning are key strategies for hospitals to lower the exposure to high capacity utilization.

Indexed as

Hospitals, GeneralHospital MortalityHumansLongitudinal StudiesRetrospective StudiesSwitzerlandCapacity utilizationCausal effectIn-hospital mortalityTime-varying covariates

Identifiers

PMID36536376
PMCPMC9764719
OpenAlexW4312093585

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.