Derivation and Validation of a Prognostic Model to Predict 6-Month Mortality in an Intensive Care Unit Population

Hadique, Sarah; Culp, Stacey; Sangani, Rahul; Chapman, Kyle; Parker, John E.; Moss, Alvin H.

doi:10.1513/annalsats.201702-159oc

Cited by 23 publications

(38 citation statements)

References 38 publications

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“…Characteristics of the mortality prediction models and underlying derivation cohorts are presented in Table 1. In all, 19 mortality prediction models (44%) were developed using prospectively collected data specifically gathered for the development of the prediction model, 6,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] whereas 24 (56%) were developed using either retrospective data [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] or prospective data previously collected for other purposes. [45][46][47][48][49] The start of data collection for the development cohorts spanned 36 years (1979-2015), and the duration of the cohort studies varying from 2 months up to 10 years for each cohort.…”

Section: Characteristics Of the Included Mortality Prediction Modelsmentioning

confidence: 99%

Mortality prediction models in the adult critically ill: A scoping review

Keuning

Kaufmann

Wiersema

et al. 2019

Acta Anaesthesiol Scand

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Section: Characteristics Of the Included Mortality Prediction Modelsmentioning

confidence: 99%

Mortality prediction models in the adult critically ill: A scoping review

Keuning

Kaufmann

Wiersema

et al. 2019

Acta Anaesthesiol Scand

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“…The various disease entities to which the SQ has been applied thus far include cancer ( 1 – 5 ), chronic obstructive pulmonary disease (COPD) ( 6 ), nephrological diseases ( 7 – 13 ), as well as pediatric palliative care ( 14 ), intensive care ( 15 ), emergency care ( 16 , 17 ), and in elderly care ( 17 – 19 ). The SQ has yet to be applied to neurological care where patients are characterized by different disease trajectories compared to other disease entities, especially cancer patients.…”

Section: Introductionmentioning

confidence: 99%

The “Surprise Question” in Neurorehabilitation—Prognosis Estimation by Neurologist and Palliative Care Physician; a Longitudinal, Prospective, Observational Study

et al. 2018

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Background: The 12-months “surprise” question (12-SQ) for estimating prognosis and the need for integrating palliative care (PC) services has not yet been investigated for neurological patients.Objective: Test the value of the 12-SQ on a sample of neurorehabilitation patients.Methods: All patients newly registered in the Department of Neurorehabilitation, Dr. Becker Rhein-Sieg-Clinic (8/2016-03/2017) were asked to participate. The treating neurorehabilitation physicians (NP) and an external consulting PC physician (PCP) independently estimated patients' prognosis using the 12-SQ; while symptom burden was independently assessed using the standardized palliative outcome measurement HOPE-SP-CL, a set of additional neurological issues, and ECOG. Follow-up with consenting patients 12 months later was via telephone. Descriptive and inferential statistics were utilized in data analysis.Results: Of 634 patients, 279 (44%) patients (male: 57.7%, female: 42.3%; mean age: 63 ± 14) (or, alternatively, their legal representative) consented and were assessed at baseline. Per patient NP and PCP both answered the 12-SQ with “Yes” (164), with “No” (42), or had different opinions (73). The “No” group displayed the highest symptom burden on all three measures for both disciplines. Overall, PCP scored higher (i.e., worse) than NP on all measures used. Follow-up was possible for 236 (drop-out: 15.4%) patients (deceased: 34 (14.4%), alive: 202 (85.6%)). Baseline scores on all measures were higher for deceased patients compared to those still living. Prognostic characteristics were: sensitivity: NP 50%, PCP 67.6%; specificity: NP 86.1%, PCP 70.3%, p < 0.001; positive predictive value: NP 37.8%, PCP 27.7%; negative predictive value: NP 91.1%, PCP 92.8%; area under the curve: NP 0.68, PCP 0.69; success rate: NP 80.9%, PCP 69.9%, p = 0.002. Regression analysis indicated that age, dysphagia and overburdening of family (NP answering the 12-SQ), dysphagia and rehabilitation phase (PCP answering the 12-SQ) were associated with increased likelihood of dying within 12 months. Without the 12-SQ as relevant predictor, age, dysphagia and ECOG were significant predictors (NP and PCP).Conclusion: Combining the 12-SQ with a measurement assessing PC and neurological issues could potentially improve the 12-SQ's predictive performance of 12-month survival and help to identify when to initiate the PC approach. Clinical experiences influence assessment and prognosis estimation.

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“…These types of models have proved to be useful in other clinical contexts for the integration of PC in units such as ICUs, for which Hadique et al [6] developed a predictive model.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Models Associated with 6-Month Survival of Patients Admitted to Nursing Homes

et al. 2018

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Background: Health status and the needs presented by people admitted to nursing homes make it necessary to contemplate aspects such as prognosis to offer quality palliative care. Objective: To compare the prognostic utility in nursing homes of two prognostic models of 6-month survival based on the Palliative Prognostic Index (PPI) or Palliative Performance Status (PPS) instruments and palliative needs indicators. Methods: A longitudinal prospective observational and analytical cohort study of survival and prognostic models in 88 patients with palliative needs (assessed by the NECPAL-ICO-CCOMS^©) from an Andalusian (Spain) nursing home was performed. Sociodemographic and clinical variables were assessed, and 6 months later, in September 2017, survival was checked. Multiple logistic regression analysis was performed using the R-Commander program (version 3.2.2). Results: Two models of the logistic regression analysis met the fit criteria. The two models combined the Surprise Question, the presence of persistent symptoms, and the clinical indicators of severity from the NECPAL tool, in addition to the Charlson Comorbidity Index, and varied only in terms of the latter variable, including the PPI in the first model and the PPS in the second. In the first model, significant associations were identified between 6-month survival and the persistent symptoms variable (OR = 7.78, p = 0.025, 95% CI = 1.45–60.92) and PPI (OR = 1.94, p < 0.001, 95% CI = 1.21). In the second model, 6-month survival was also significantly associated with the persistent symptoms variable (OR = 4.57, p = 0.045, 95% CI = 1.07–22.41) and the PPS (OR = 0.93, p = 0.001, 95% CI = 0.88–0.96). Conclusions: Prognostic models such as ours that include variables commonly included in clinical assessments can help nursing home professionals prioritize and ensure adequate mobilization of palliative care resources, which are very limited in these institutions.

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Derivation and Validation of a Prognostic Model to Predict 6-Month Mortality in an Intensive Care Unit Population

Abstract: Our integrated prognostic model, which includes the SQ, has strong discrimination and calibration to predict 6-month mortality in medical ICU patients. This model can aid clinicians in identifying ICU patients who may benefit from the integration of palliative care into their treatment.

Cited by 23 publications

References 38 publications

Mortality prediction models in the adult critically ill: A scoping review

Mortality prediction models in the adult critically ill: A scoping review

The “Surprise Question” in Neurorehabilitation—Prognosis Estimation by Neurologist and Palliative Care Physician; a Longitudinal, Prospective, Observational Study

Prognostic Models Associated with 6-Month Survival of Patients Admitted to Nursing Homes

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