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作者：Pauline Anderson  
出處：WebMD醫學新聞

　　December 4, 2009 — 研究者發展並確認一種用於血液透析(HD)病患的新的預測存活工具。
　　
　　研究作者、麻塞諸塞州春田市Baystate醫學中心腎臟小組醫學系Michael J. Germain醫師表示，這個新的預後模式包括五個變項且容易使用，將可解決腎臟科醫師與病患溝通壞消息時的障礙－缺乏準確的預後工具。
　　
　　Germain醫師表示，醫師們不願意告訴病患預後的理由之一是，醫師們對於猜測的、不準確的預後資訊感到不安。
　　
　　這項研究的目標是進行一個縱向前瞻性世代研究，並發展與確認一套整合預後模式，準確預測HD病患的六個月存活情況。
　　
　　為了建立這個新的預後模式，Germain醫師等人使用精算因素(例如年紀、白蛋白、共病異常)、醫師預測(驚訝問題("surprise" question [SQ])：如果此病患死亡，我是否會感到驚訝？)以及死亡率分析。
　　
　　研究者發展一個有512名在西麻塞諸塞州5個透析診所接受HD之病患的起源世代。根據病歷回顧與訪談，研究者蒐集精算預測因子的資訊，並詢問腎臟科醫師，如果他們的病患在未來六個月內死亡，是否會感到驚訝。
　　
　　他們接著蒐集確認世代的514名病患(來自八個(原來的五個診所加另外三個診所)新英格蘭的HD診所)的類似資訊。
　　
　　完成研究時，「不驚訝組」(腎臟科醫師對於前述[SQ]問題的回答為不者) 病患有54.9%死亡，「驚訝組」(腎臟科醫師對於前述[SQ]問題的回答為是者)有17%病患死亡，校正風險比(HR)為4.88。
　　
　　SQ是起源世代中與提早死亡有關的五個獨立變項之一，SQ(「不驚訝組」vs「驚訝組」)的HR為2.71。其他變項包括年長(每增加10歲則HR為1.36)、失智(HR為2.24)、週邊血管疾病(HR為1.88)以及白蛋白(每增加1單位則HR為0.27)。
　　
　　當使用一個整合這五個變項的模式時，研究者在起源組測得的六個月存活預測曲線下面積為0.87 (95%信心區間為0.82 - 0.92)，在12個月存活和18個月存活時，此數據分別是0.82和0.79。
　　
　　於確認世代檢視此一模式時，研究者發現，在這一組的六個月死亡率有類似的預測數據：0.80 (95%信心區間為0.73 - 0.88)。
　　
　　作者們寫道，整合預後模式本身讓病患的風險層化，任一組成因素(例如SQ)都比較特定與敏感，似乎可以改善現有的透析病患存活預測工具。再者，此預後模式在研究組和臨床運用時都有足夠的準確度。
　　
　　使用此工具時，醫師們只要打開電腦上的空白表格程式或者登入一個網站，在適當的五個領域中輸入數據和答案，然後立即可以獲得預後。Germain醫師表示，這只要花個5秒鐘，醫師們也可以在手持式裝置上運作，我們有建立一個iPhone運用程式。
　　
　　Germain醫師表示，雖然腎臟科醫師不想因為不佳的預後而澆滅病患的希望，但研究顯示，超過90%的透析病患希望知道自己的預後，他們想聽到醫師告知的訊息。病患和家屬感到釋懷，他們不會因為聽到預後而產生憂鬱或有負面反應，因為事先被告知預後訊息，在病患死亡後，比較容易面對悲傷。
　　
　　Germain醫師表示，研究者正計畫一個更大型的研究，將比較接受一般照護的一組病患、和一組從使用這個新工具的腎臟科醫師和社工師獲得預後訊息的病患。這個多中心研究的主要結果，將評估此預後工具是否會影響住院和安寧照護。
　　
　　加入負責溝通訓練的社工師到這個團隊，有助於委婉的告知壞消息，因此也可以解決腎臟科醫師缺少時間的問題。
　　
　　研究者指出，因為兩個世代都來自單一地區(新英格蘭)，當用於其他地區或不同族群的診所時，還需確認此模式是否一樣準確。
　　
　　他們也發現，研究中並沒有足夠的醫師，而無法有意義地分析腎臟科醫師訓練、經驗和SQ預測準確度之間的關聯。
　　
　　作者們表示，這個預測模式可以藉由納入其他變項如C反應蛋白、血色素、鈣值與身體質量指數而加強。
　　
　　作者們皆宣告沒有相關財務關係。
　　
　　Clin J Am Soc Nephrol.P線上發表於2009年12月3日。


Six-Month Prognostic Tool May Improve End-of-Life Care for Hemodialysis Patients

By Pauline Anderson
Medscape Medical News

December 4, 2009 — Researchers have developed and validated a new tool for predicting survival of patients receiving hemodialysis (HD).

The new prognostic model, which is easy to use and includes 5 variables, will break down one of the main barriers preventing nephrologists from communicating bad news to patients — lack of an accurate prognostic tool, said study author Michael J. Germain, MD, from the Department of Medicine, Section of Nephrology, Baystate Medical Center, Springfield, Massachusetts.

"One of the reasons doctors don't like giving a prognosis to patents is that they're uncomfortable because they don't feel they have accurate information about prognosis; it's just kind of guessing," Dr. Germain said.

The goal of this research was to conduct a longitudinal prospective cohort study and to develop and validate an integrated prognostic model with sufficient accuracy to predict 6-month survival for patients receiving HD.

To build the new prognostic model, Dr. Germain and colleagues used actuarial factors (eg, age, albumin, comorbid disorders), clinician predictions (the "surprise" question [SQ]: "Would I be surprised if this patients died?"), and mortality analyses.

The researchers developed a derivation cohort of 512 patients receiving HD at 5 dialysis clinics in western Massachusetts. From chart reviews and interviews, the researchers gathered information on actuarial predictors and asked nephrologists whether they would be surprised if their patient died within the next 6 months.

They then collected similar information for a validation cohort of 514 patients at 8 New England HD clinics (the 5 original plus 3 additional clinics).

At the completion of the study, 54.9% of the "no" group of patients (those for whom nephrologists had answered 'no' to the SQ) had died compared with 17% of the "yes" group patients, with an unadjusted hazard ratio (HR) of 4.88.

SQ was 1 of 5 variables that were independently associated with early mortality in the derivation cohort. The SQ (not surprised vs surprised) had an HR of 2.71. The other variables were older age (HR for a 10-year increase, 1.36), dementia (HR, 2.24), peripheral vascular disease (HR, 1.88), and albumin (HR expressed for a 1-U increase, 0.27).

When using a model that integrates all 5 variables, the researchers determined that the area under the curve for predictions of 6-month survival in the derivation group was 0.87 (95% confidence interval, 0.82 - 0.92). The figures for 12- and 18-month survival were 0.82 and 0.79, respectively.

When testing this prognostic model in the validation cohort, the researchers found that the comparison prognostic figure for 6-month mortality in this group was 0.80 (95% confidence interval, 0.73 - 0.88).

"The integrated prognostic model lends itself to risk stratification of patients, it is more specific and sensitive than any of its components (e.g., the SQ), and it seems to be a considerable improvement over other existing instruments at predicting survival in the dialysis population," the authors write.

Furthermore, this prognostic model is sufficiently accurate for both research and clinical applications, they said.

To use the tool, physicians would simply open a spreadsheet on their computer or log onto a Web site and type in the numbers and answers in the appropriate 5 fields, and they will immediately get the prognosis. "It will literally take just 5 seconds to do," said Dr. Germain. "Or they could have it on a handheld device — we could have it as an iPhone application."

Although nephrologists do not want to extinguish a patient's hopes by delivering a poor prognosis, research shows that more than 90% of dialysis patients want to know their prognosis, and they want to hear the news from their own physician, said Dr. Germain. "Patients and families are relieved, and they don't get depressed or have a negative response to hearing their prognosis. Even after a patient has died, his or her family feels they are better adapted to the grief when told the prognosis ahead of time."

The researchers are planning a larger study that will compare a group of patients receiving usual care with another group receiving their prognosis derived using the new tool from both a nephrologist and a social worker. The main outcome of this multicenter study will be to see whether this prognostic tool affects hospital and palliative care, said Dr. Germain.

Adding a social worker, who has communication training, to the team helps in delicately relaying bad news. It also eliminates another barrier for nephrologists — lack of time.

Because both cohorts were drawn from a single geographical area (New England), "it remains to be seen whether the resultant model may be as accurate in other regions or in clinics with differing populations," the researchers said.

They also noted that there were not enough physicians in the study to allow for meaningful analysis of potential associations between nephrologist training, experience, and accuracy of SQ predictions.

The prognostic model may eventually be strengthened with the inclusion of other variables — for example, C-reactive protein, hemoglobin, and calcium levels and body mass index — said the authors.

The authors have disclosed no relevant financial relationships.

Clin J Am Soc Nephrol. Published online December 3, 2009.