Rationing of Intensive Care Unit Services. An Everyday Occurrence

Strauss, Michael J.; LoGerfo, James P.; Yeltatzie, James A.; Temkin, Nancy; Hudson, L. D.

doi:10.1097/00132586-198610000-00043

Cited by 52 publications

(74 citation statements)

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“…It has been documented that physicians tend to be more selective in their ICU admissions during times of bed shortages, with patients having higher severity of illness being admitted. 20 Critically ill patients usually deteriorate when managed outside ICU while waiting for availability of ICU beds. Poor nutritional status at ICU admission may also be one of the important causes contributing to high mortality rate in India.…”

Section: Discussionmentioning

confidence: 99%

Assessment and comparison of 3 mortality prediction models SAPS II, APACHE II and SOFA for prediction of mortality in patients of sepsis

et al. 2017

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Background: Little is known about outcomes of patients admitted to the ICU with severe sepsis and septic shock, despite the seriousness of sepsis as a public health problem in developing countries. Understanding sepsis outcome studies is hampered by lack of an agreed severity of illness scoring system for sepsis patients. The objective of the present study is to assess and compare the validity of 3 mortality prediction models SAPS 2, APACHE II and SOFA for prediction of mortality in patients of sepsis.Methods: One hundred patients of Sepsis were selected after applying the inclusion and exclusion criteria. Informed consent was taken from the patients or their relatives A careful and detailed history was recorded to assess the onset and duration of clinical events and the probable risk factors for the same; a detailed general physical examination was performed. Blood sampling for CBC, RFT, LFT and arterial blood gas analysis was done. SAPS 2, APACHE II and SOFA scores were calculated on the day of admission.Results: The ROC analysis shows that the best discrimination was provided by SAPS 2 score (AUROC=0.981), followed by APACHE II (AUROC=0.978) and SOFA (AUROC=0.911).Conclusions: SAPS 2 score was superior to the APACHE II and SOFA scores for predicting survival in patients with septic shock but a combination of factors must be taken in consideration to estimate the prognosis in the ICU.

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Section: Discussionmentioning

confidence: 99%

Assessment and comparison of 3 mortality prediction models SAPS II, APACHE II and SOFA for prediction of mortality in patients of sepsis

et al. 2017

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“…We have defined "ICU capacity strain" as a set of temporally varying influences on the ability of an ICU to provide high-quality care for everyone who is or could become a patient in that ICU on that day (7). Several time-varying ICU factors are associated with patient outcomes, including ICU census, the proportion of new admissions, the mean acuity of other ICU patients, and bed occupancy (8)(9)(10)(11)(12)(13)(14). However, not all studies have documented such associations (15), and the impact of these and other variables on other potential consequences of capacity strain is unclear.…”

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confidence: 99%

Objective Factors Associated with Physicians’ and Nurses’ Perceptions of Intensive Care Unit Capacity Strain

et al. 2014

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Rationale: Time-varying demand for critical care may strain the capacities of intensive care units (ICUs) to provide optimal care. Intensivists and ICU nurses may be the best judges of the strain on their ICU. Yet, it is not clear what ICU and hospital factors contribute to this perceived sense of strain among ICU providers.Objectives: To identify measureable ICU and hospital factors associated with perceived strain by intensivists and ICU nurses.Methods: During a 6-month prospective cohort study, we surveyed nurses and physicians responsible for bed management regarding the ability of a 24-bed medical ICU (MICU) to provide optimal critical care. We simultaneously assessed time-varying ICU-level factors, including patient census, number of admissions, average patient acuity, number of interhospital transfer requests, and censuses of other hospital units. To identify factors associated with strain, we used an algorithm for covariate selection in regression models that selects variables that contribute sufficiently to model prediction to justify their inclusion. Measurements and Main Results:Of 254 surveys, 226 (89%) were completed by 18 charge nurses and 17 physicians. On a scale of 1 to 10 (where a higher score indicated more strain), the median perceived strain score among nurses was 6 (interquartile range, 3-7) and among physicians was 5 (interquartile range, 3-7), with moderate correlation within days (interclass correlation coefficient, 0.45; 95% confidence interval: 0.30, 0.60). Average patient acuity, MICU census, number of MICU admissions, and general ward census were included in the most efficient model of strain perceived by nurses. Only MICU census was strongly associated with strain perceived by physicians. Conclusions:A model containing commonly available metrics of ICU census, average patient acuity, and the proportion of new admissions has validity as a model of ICU nurses' perceived ICU capacity strain. However, only ICU census was associated with increased perceived capacity strain by physicians, highlighting the need for involvement of multiple stakeholder groups to improve our understanding of ICU capacity strain.

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“…19"22 Since most studies of intensive care are limited to patients in the MICU, they suffer from selection bias. 23,24 It is important to evaluate how well any set of triage criteria predicts whether the high-risk pa¬ tients admitted from the emergency department and the medical service will require MICU therapy. This entails ap¬ plying admission criteria to patients outside the MICU, as well as observing what happens to those admitted to the MICU.20…”

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confidence: 99%