New Symptom-Based Predictive Tool for Survival at Seven and Thirty Days Developed by Palliative Home Care Teams

To cite this article:
NabalMaria, BescosMar, BarconsMiquel, TorrubiaPilar, TrujillanoJavier, and RequenaAntonio. Journal

Author information

Maria Nabal, MD, PhD,¹ Mar Bescos, MD,² Miquel Barcons, MD,³ Pilar Torrubia, MD,⁴ Javier Trujillano, MD, PhD,⁵ and Antonio Requena, MD, PhD⁶

¹Palliative Care Supportive Team, Hospital Universitario Arnau de Vilanova, Lleida, Institut Català de la Salut, IRB Lleida, Spain.

²Family and Comunity Medicine, Home Care Support Team Huesca (ESAD Huesca), Aragon Health Sciences Institute, Huesca, Spain.

³Family and Community Medicine, Home Care Support Team (PADES Granollers), Institut Català de la Salut, Spain.

⁴Family and Comunity Medicine, Home Care Supportive Team (ESAD Zaragoza), Aragon Health Sciences Institute, Zaragoza, Spain.

⁵Department of Basic Medical Sciences, University of Lleida, Lleida, Spain.

⁶Family and Community Medicine, Medical Emergency Unit Alcañiz, Aragon Health Sciences Institut, Spain.

Address correspondence to:

Maria Nabal

UFISS Cuidados Paliativos.

Hospital Universitario Arnau de Vilanova.

Av. Rovira Roure 80.

Lleida 25198

Spain
E-mail: mnabal@secpal.com

Accepted April 4, 2014

ABSTRACT

Aim: This study sought to develop models to predict survival at 7 and 30 days based on symptoms detected by palliative home care teams (PHCTs).

Materials and methods:

This prospective analytic study included a 6-month recruitment period with patient monitoring until death or 180 days after recruitment. The inclusion criteria consisted of age greater than 18 years, advanced cancer, and treatment provided by participating PHCTs between April and July 2009. The study variables included death at 7 or 30 days, survival time, age, gender, place of residence, type of tumor and extension, presence of 11 signs and symptoms measured with a 0–3 Likert scale, functional and cognitive status, and use of a subcutaneous butterfly needle. The statistics applied included a descriptive analysis according to the percentage or mean±standard deviation. For symptom comparison between surviving and nonsurviving patients, the χ² test was used. Classification and regression tree (CART) methodology was used for model development. An internal validation system (cross-validation with 10 partitions) was used to ensure generalization of the models. The area under the receiver operating characteristics (ROC) curve was calculated (with a 95% confidence interval) to assess the validation of the models.

Results:

A total of 698 patients were included.

The mean age of the patients was 73.7±12 years, and 60.3% were male.

The most frequent type of neoplasm was digestive (37.6%).

The mean Karnofsky score was 51.8±14, the patients' cognitive status according to the Pfeiffer test was 2.6±4 errors, and 8.3% of patients required a subcutaneous butterfly needle.

Each model provided 8 decision rules with a probability assignment range between 2.2% and 99.1%.

The model used to predict the probability of death at 7 days included the presence of anorexia and dysphagia and the level of consciousness, and this model produced areas under the curve (AUCs) of 0.88 (0.86–0.90) and 0.81 (0.79–0.83).

The model used to predict the probability of death at 30 days included the presence of asthenia and anorexia and the level of consciousness, and this model produced AUCs of 0.78 (0.77–0.80) and 0.77 (0.75–0.79).

Conclusion: For patients with advanced cancer treated by PHCTs, the use of classification schemes and decision trees based on specific symptoms can help clinicians predict survival at 7 and 30 days.