Dose‐response analyses using restricted cubic spline functions in public health research

Desquilbet, Loïc; Mariotti, François

doi:10.1002/sim.3841

Cited by 1,376 publications

(1,004 citation statements)

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“…The dose-response relationship between inhospital mortality and BMI was calculated using a restricted cubic spline function fitted for a logistic regression model after testing the overall association and linearity using the percentage RCS Reg SAS macro developed by Desquilbet and Mariotti. 22 Odds ratios and 95% CI were calculated for BMI values with respect to the reference value of 23.0 kg/m 2 after controlling simultaneously for all potential confounding factors. To adjust for clustering within hospitals, generalized estimating equations 23 were used.…”

Section: Resultsmentioning

confidence: 99%

The Dose-Response Relationship Between Body Mass Index and Mortality in Subjects Admitted to the ICU With and Without Mechanical Ventilation

et al. 2015

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BACKGROUND: Obesity has been associated with increased mortality in the general population, whereas a paradoxical relationship between higher body mass index and lower mortality has been referred to as the obesity paradox in critically ill patients. However, it remains unknown whether a particular subgroup is most affected. The aim of the present study is to elucidate whether obesity is associated with lower mortality in the ICU population by comparing subjects with and without mechanical ventilation. METHODS: A total of 334,238 subjects from a nationwide database who were discharged between July 2010 and March 2012 and who were admitted to general ICUs during their hospitalization were included in this study. The primary outcome was in-hospital mortality. RESULTS: Of all subjects evaluated, 23.3% were started on mechanical ventilation within the first 2 d after ICU admission. Compared with the non-ventilated group, the ventilated group was more likely to have sepsis, pneumonia, or coma. The ventilated group underwent more procedures within the first 2 d after ICU admission compared with the non-ventilated group. A restricted cubic spline function showed lower mortality in subjects with a higher body mass index among the ventilated group, whereas mortality was increased with increasing body mass index in the non-ventilated group. CONCLUSIONS: This study shows that a high body mass index is associated with low mortality in the mechanically ventilated group, whereas the non-ventilated group showed a reverse J-shaped association. There was a higher mortality rate in underweight subjects in both groups.

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Section: Resultsmentioning

confidence: 99%

The Dose-Response Relationship Between Body Mass Index and Mortality in Subjects Admitted to the ICU With and Without Mechanical Ventilation

et al. 2015

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“…We used multivariable logistic regression to calculate the odds ratios (ORs) for the 4-year incidences of the outcomes between eosinophil counts and a reference count of 0.16 3 10 9 /l which was the median eosinophil count in our data. These ORs were adjusted for known and possible confounders such as sex, age, year, and month of DIFF sampling, CRP (as a surrogate marker for increased inflammation), previous DIFF sampling and competing comorbid conditions (CCI), and modeled as a restricted cubic spline [15]. The potential confounders were included as additional variables in the logistic regression models.…”

Section: Methodsmentioning

confidence: 99%

Association of the blood eosinophil count with hematological malignancies and mortality

et al. 2015

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Blood eosinophilia (≥0.5 × 109/l) may be an early sign of hematological malignancy. We investigated associations between levels of blood eosinophils and risks of hematological malignancies and mortality in order to provide clinically derived cut‐offs for referral to specialist hematology care. From the Copenhagen Primary Care Differential Count (CopDiff) Database, we identified 356,196 individuals with at least one differential cell count encompassing the eosinophil count during 2000–2007 and matched these laboratory data with Danish nationwide health registers. We used multivariable logistic regression to calculate odds ratios (ORs) for the 4‐year incidences of hematological malignancies and mortality between the eosinophil counts and a reference count of 0.16 × 109/l which was the median eosinophil count in our data. Risks of hematological malignancies and mortality increased above the median eosinophil count. At the 99th percentile, corresponding to an eosinophil count of 0.75 × 109/l, risks of hematological malignancies were increased more than twofold with OR (95% C.I.) of 2.39 (1.91–2.99). Interestingly, risks reached a plateau around an eosinophil count of 1.0 × 109/l. Risks also increased when the eosinophil count approached zero. Here, counts associated relatively more with acute myeloid leukemia and myelodysplastic syndromes whereas counts above 0.16 × 109/l associated more with myeloproliferative neoplasms. Eosinophil counts associate with hematological malignancies and mortality even below the definition of eosinophilia. The observed plateau of risks around 1.0 × 109/l is important for physicians encountering patients with eosinophilia since even mild‐to‐moderate eosinophilia according to traditional definitions confers maximally increased risks of subsequent/subclinical hematological malignancy. Am. J. Hematol. 90:225–229, 2015. © 2014 Wiley Periodicals, Inc.

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“…The latter was done by way of linear splines. First, restricted cubic spline models were used (data not shown) as inference to help us to see the shape of the curves and choose the knots (or cut points) for linear splines 12. SI was modeled as cubic splines with knots at 5th, 50th and 95th percentiles of SI.…”

Section: Methodsmentioning

confidence: 99%

Shock Index Predicts Patient‐Related Clinical Outcomes in Stroke

Myint

Sheng

Xian

et al. 2018

JAHA

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BackgroundThe prognostic value of shock index (SI), heart rate divided by systolic blood pressure, in stroke for clinical outcomes other than mortality is not well understood.Methods and ResultsWe examined the Get With The Guidelines–Stroke (GWTG‐Stroke) data to explore the usefulness of SI in predicting in‐hospital outcomes in 425 808 acute stroke cases (mean age: 71.0±14.5 years; 48.8% male; 89.7% ischemic stroke and 10.3% intracerebral hemorrhage) admitted between October 2012 and March 2015. Compared with patients with SI of 0.5 to 0.7, patients with SI >0.7 (13.6% of the sample) had worse outcomes, with adjusted odds ratios of 2.00 (95% confidence interval [CI], 1.92–2.08) for in‐hospital mortality, 1.46 (95% CI, 1.43–1.49) for longer length of hospital stay >4 days, 1.50 (95% CI, 1.47–1.54) for discharge destination other than home, 1.41 (95% CI, 1.38–1.45) for inability to ambulate independently at discharge, and 1.52 (95% CI, 1.47–1.57) for modified Rankin Scale score of 3 to 6 at discharge. Results were similar when analyses were confined to those with available National Institutes of Health Stroke Scale (NIHSS) or within individual stroke subtypes or when SI was additionally included in the models with or without blood pressure components. Every 0.1 increase in SI >0.5 was associated with significantly worse outcomes in linear spline models. The addition of SI to existing GWTG‐Stroke mortality prediction models without NIHSS demonstrated modest improvement, but little to no improvement was noted in models with NIHSS.Conclusions SI calculated at the point of care may be a useful prognostic indicator to identify those with high risk of poor outcomes in acute stroke, especially in hospitals with limited experience with NIHSS assessment.

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Dose‐response analyses using restricted cubic spline functions in public health research

Cited by 1,376 publications

References 42 publications

The Dose-Response Relationship Between Body Mass Index and Mortality in Subjects Admitted to the ICU With and Without Mechanical Ventilation

The Dose-Response Relationship Between Body Mass Index and Mortality in Subjects Admitted to the ICU With and Without Mechanical Ventilation

Association of the blood eosinophil count with hematological malignancies and mortality

Shock Index Predicts Patient‐Related Clinical Outcomes in Stroke

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