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PredictSurvival.com
Introduction, FAQ, and Contact Information

Introduction:

There are multiple validated tools to estimate prognosis in patients with advanced cancer. However, there are several barriers towards their regular use in clinical practice:

  1. It is difficult to choose which tool to use.
  2. The calculation can be cumbersome, especially with multiple models.
  3. The interpretation can be challenging.

In an attempt to try and overcome these challenges, we have created this website to provide survival estimates based on multiple prognostic models based entirely on published results that are referenced.

Currently, the models we have included are:

As well as predictions based solely on the following Performance Status Scales:

Additionally, we include a calculator that may be useful for the physician dealing with a patient where survival is estimated to be days to weeks. In this scenario, it is often helpful to know whether the patient is approaching impending death (less than 72 hours of survival), as that can help guide the adjustment of palliative interventions, referral to Acute Inpatient Palliative Care Units, and provide better guidance for patients and families.

Data obtained by Dr. Hui and collaborators, studying the association between survival and numerous clinical signs in patients admitted to Acute Palliative Care Units, allowed them to develop a model via recursive partition analysis (RPA) to predict mortality. In addition, derivation of positive and negative likelihood ratios (LRs) for these clinical signs can be used to predict death, provided one is able to generate a reasonable pretest probability. Our impending death calculator combines the RPA model along with a simple pre-test probability / post-test probability calculator using positive and negative LRs.8-10


This is an entirely non-profit endeavor to promote these tools in clinical practice and to advance research in this area. It is a calculator only and no information is being saved or stored. This work is not endorsed by nor affiliated with our respective employers. If you have any questions about how it works feel free to look at the source code that is linked below (or email JP Maxwell). It is open licensed and thus free to build upon, improve or reuse if you wish.

Created by David Hui MD, MSc, Associate Professor of Oncology and Palliative Care at MD Anderson in Houston, Texas and JP Maxwell MD, a Hospitalist at Virginia Mason Memorial Hospital in Yakima, Washington.

FAQ:

Q: Why don't you include prognostic model X, Y or Z?

A: There are a lot of prognostic models out there and more are being developed all the time. Some are more validated than others, some are more specific for certain cancer or disease types, and some are proprietary. At the moment, we are focusing on nonspecific cancer prognostic calculators that are well validated and do not have restrictions on their use. If you have any suggestions about other prognostic models please email us!

Q: The website is blocked at the hospital/clinic I work, but I can use it at home, why?

A: Many hospitals use extra security software programs that check websites against their own "whitelist" of known, safe website domains to prevent hackers from stealing protected health information. Since our website is new (the domain was registered in early March 2017) it may be several weeks to months before each individual software company tests and approves it. If you find the website blocked, I would appreciate an email (JP Maxwell) with the name of the software program blocking it so I can contact the company and get the website whitelisted sooner.

Q: Where was the background picture taken?

A: This was taken on a hike in the foothills surrounding the Yakima Valley in the fall after some rain.

Contact:

We are continually refining the website based on our own use, the addition of new publications and models, as well as feedback from other providers so if you have any suggestions, notice an error or have a criticism please don't hesitate to email us:

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Original Papers for Prognostic Models and Performance Status/Scales:
  1. Maltoni M, Nanni O, Pirovano M, et al. Successful validation of the Palliative Prognostic Score in terminally ill cancer patients. Italian Multicenter Study Group on Palliative Care. J Pain Symptom Manage. 1999;17:240-247.
  2. Scarpi E, Maltoni M, Miceli R, Mariani L, Caraceni A, Amadori D, Nanni O. Survival prediction for terminally ill cancer patients: revision of the palliative prognostic score with incorporation of delirium. Oncologist. 2011;16(12):1793–9.
  3. Morita T, Tsunoda J, Inoue S, Chihara S. Survival prediction of terminally ill cancer patients by clinical symptoms: development of a simple indicator. Jpn J Clin Oncol. 1999;29:156-159.
  4. Yamada T, Morita T, Maeda I, et al. A prospective, multicenter cohort study to validate a simple performance status–based survival prediction system for oncologists. Cancer. 2016; doi:10.1002/cncr.30484
  5. Anderson F, Downing GM, Hill J, et al. Palliative Performance Scale (PPS): A New Tool. J Palliative Care. 1996;12(1):5-11.
  6. Karnofsky DA, Abelmann WH, Craver LF, Burchenal JH. The Use of the Nitrogen Mustards in the Palliative Treatment of Carcinoma, With Particular Reference to Bronchogenic Carcinoma. Cancer. 1948;1(4):634-656.
  7. Oken MM, Creech RH, Tormey DC, et al. Toxicity And Response Criteria Of The Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5:649-655.
  8. Hui D, Hess K, Dos Santos R, et al. A Diagnostic Model for Impending Death in Cancer Patients: Preliminary Report. Cancer 2015;121(21): 3914-3921.
  9. Hui D, Dos Santos R, Chisholm G, et al. Bedside clinical signs associated with impending death in patients with advanced cancer: preliminary findings of a prospective, longitudinal cohort study. Cancer. 2015; 121(6):960-7.
  10. Hui D, Dos Santos R, Chisholm G, et al. Clinical signs of impending death in cancer patients. Oncologist. 2014; 19(6):681-7.
  11. Suh SY, Choi YS, Shim JY, et al. Construction of a new, objective prognostic score for terminally ill cancer patients: a multicenter study. Support Care in Cancer. 2010 Feb;18(2):151-7.