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Bedside charting of pain levels in hospitalized patients with cancer: A randomized controlled trial
Ori@naZArticle
Bedside Charting of Pain Levels in Hospitalized Patients with Cancer: A Randomized Controlled Trial
Znbvductbn Pain is among rhc mos common--and most feared-manifestarions of rancrr. Althwgh precise epidemiologic tiara are iarking. C%t?,90% of adulrs bith progrcsrivc disceasereport substantial pain, and appwximawly one-Ihird
care of cancer-related pain within healthcare institutions is often poor. Many barriers to highquality cancer pain management have been identified, including problems related to ptactitioners (e.g., ignorance of current guidelines; fear of causing addiction), patients (e.g., rcluclance IO report pain and to take pain mcdications). and the health care system (e.g.. inadequate reimtnusemen1 and restrictive regulation of analgesics).’ Inadequate assessment of pain constilutes a further barrier. Failure IO conduct an adequate assessment of pain is recognized as an important reason for undertreatment. Health care providers need IO ask patients ahout pain. because patients are often reluctant IO vohtntrer this information.” Furthermore, clinicians perform poorly when trying to cdmatc patients’ degree of pain without asking them directly.’ A number of reliable and salid pain assessment tools are now available.’ Nevertheless. assessment of patients’ pain will not lead to belter care unless the information is packaged in a way that practitioners can believe it, understand it, and use it. Because undcrstanding is a prerequisite IO appropriate therapeutic action. providing physicians with information on the status of their patients may improve outcomes.” I’hysicians and nurses are accustomed to recording and using clinical information displayed in graphical form. A glance at the vital signs sheer. for example, may tell the physician that the blood pressure is trending down, prompting immediate action. To test tbe hypothesis that visual display of cancer patients’ pain levels might improve their treatment, we conducted a randomized controlled trial. Patients assigned to the intervention group had daily pain assessmentsby study staff who graphically recorded their reported pain intensity on bedside wall charts. Control group patients had pain assessmentsby study staff hut did not have this information displayed. In conducting this trial, we addressed rhrcc principal research questions. First. did the intervention lead IO lower pain intensity during the hospitalization? Second. did the intervention affect patients’ sleep, cancer-related symptoms, or overall quality of life? Third, did the intervention influence physicians to prescribe opioid analgesics more aggressively for their patients with cancer-related pain?
MlZthOdS SPffing
The study tins conducted in early I993 at a large Univ.rsity hospital in southern California and its affiliated VA Medical Center. At both hospitals, patirnts with cancer are admitted both IO general medical services and IO specialized oncology services, and all treatment orders are written by trainees in internal medicine under faculty supervision. Patients wete cligihlz for enrollment in the smdy if :.xy were betwcen 18 and 80 years 01 age, able to speak and undenumd Enghsh. had been adnriued IO the hospital within the past 48 hr with a diagnosis of malignancy, and had at least “moderate pain” during the baseline assessment that was not obviously unrelaled lo cancer or its treatment. Each weekday morning during the study period, a trained research assistant reviewed the medical records of patients admitud during tbe past 24 hr with an admitting diagnosis of cancer (previous 72 hr following a weekend). The research assistant also contacted residents informally after morning report and on the vvarcb IO identify other potentially eligible patients. If medical record review confirmrd that the patient had active cancer, the research assistant approached the patient to assessbaseline pain staurs and request participation in the study. Patients were asked IO indicate their current pain and WOW pain in the past 24 hr using a sliding visual analog device (slide algometer distributed by The Purdue Frederick Company); reported pain could range from 0 IO 10 units. Paticnts with either current pain or worst pain of at least 2.5 units were asked for informed consent and then randomized to either the intervention or control group using a random numbers table. Of 97 eligible patients identified by the research a&tam, 87 consented to participate further (72 at the Univenity, I5 at the VA). However, only 78 patients were available for at least two follow-up pain assessments, and they condtuned the sample for this analysis. Inlervenfh The intervention patients’ self-reported
involved displaying pain levels on a spe-
cially designed sheet that was placed behind patients vital signs on a clipboard in their hospital room. Both muses and physicians were able to review the information during their daily rounds and incorporate the information into their pain management plans. “Current pain” and “worst pain past 24 hours” were charted in red ink on a O-IO scale each day. To highlight trends in symptomatology, dots (current pain) acd x’s (worst pain) were connccted by solid lines. The display sheet also contained space to record patients estimates of the number of hours sleep obtained during the past 24 hr, a short outline of pain management guidelines (in the lower lefthand corner) and an equianalgesic dosing table (in the lower righthand corner). During daily follow-up visits. the research assistant also administered a questionnaire that inquired about quality of sleep, symptoms related to cancer and its therapy. and overall quality of life, but these data were not displayed. Owing to budgetary limitations. pain data were roll -ted only once daily in the morning. The research assis tam asked the same questions of control group patients as intervention group patients, but their responses were not displayed.
MpasuSt?S Main outcomes measures included runrnf pnin [measured on both a continuous visual analogue scale (VAS; range. O-10) and a fivepoint verbal scale spanning mild to excruciating pain (range, l-5)) and -I @zrn in the past 24 hr (measured on a continuous VAS (range, O-10). Previous research has shown these measures to correlate with each other and with the McGill Pain Questionnaire.” Because it was hypothesized that the intervention would take at least 24-48 hr to affect clinician behavior and thereby patients’ pain. we measured current pain and worst pain on the third and fifth day followittg tire initial esahlation (counting the initial valuation as day 1). This allowed time for physicians to note and act upon two to four graphically displayed data points (i.e., from day I to 2 or from days 1 to 4, respectively.) Measurements were ~01 atailable for some patients on days 3 .tnd 5 because of early discharge, weekend or hohday scheddules, or absence from the wards for diagnostic testing. For these patients, we substituted measures taken on day 2 (instead of day 3) or day
4 (instead of day 5). A pain rummar) se& (for days 3 and 5) was formed by multiplying current verbal pain by 2 (making a 2-10 scale), adding the product to the cum of cut rent VAS pain plus VA.5 worst pain, and then dibidmg by 3; the resulting scale could range from 0.67 to 10. WC could not measure day 5 outcomes for 28 patients due to their having been discharged before day 4. Secondary outcomes inrhtded measures of sleep, symptoms, overall quality of life, and narcotic dosing. .Yle+ durnlion was measured as the mean number of houn of sleep reported by the patient through the first week following srudy entry. slnp loferrq was measured as the mean number of minutes taken to fall asleep each night. also as reported by the patient through the first week. A symptomscab scowwas computed as the mean intensity of six symptoms (nausea, vomiting, constipation. loss of appetite, drowsiness. and depression), measured on a I (none) to 6 (very severe) scale and averaged o+er the first week. These symptoms were selected because they are common in cancer patients and may be exacerbated by treatment with opioid analgesics. Cronbach’s alpha reliability for the sixitem symptom scale was 0.55. indicating fair internal consistency of the items. chnall quolify oJl$ was assessed on a daily basis by asking the patient to circle a number on a scale ranging from 0 (WOBL possible quality of life. denoted by a sad face) to 10 (best possible quality of life. denoted by a happy face).“&” Because we expected that making physicians more aware of their patients’ pain might encourage more aggressive analgesic prescrib ing, we computed an +ioid anaigrsia scow in the following way. First. the research a&tam searched pharmacy and n::.sing records to obtain the dose and route of any opioid analgesics administered during the previous 24 hr. Then, we converted the number of milligrams of opioid medication received by patients each day into “paremetal morphine equivalenu” (PMb). using a standard equianalgesic dosing table.’ Sext. to adjust for the amount of pain art individual patient was experiencicg (patients with Ies pain would require less analgesia), we divided that day’s PME tame by the patient’s current verbal pain score (l-5 scale). Finally, we averaged the adjusted PMl3 over study days 24.
lnrenrn~bn
chanc1etiIic Age (mea” yean)
Femsk (96) Non+vhitc(R) Bwline
current pain (mnn viwal analoguc unh)
Lhdine wont pam previou* ?4 hr (mean \iroal andognc units) Bmdine wrhd pain rating (mean) Baselineoverall qualityd-life rating kceking
opioid arulgesiio on admission (‘:o)
Worm-nlth. rompanum~
urn’ u~ltWcaIh signilicrnl
kinsale
48.6 52.6 34.2 2.96 6.51
2.10 5.20 77.5
2.05 4.50 63.1
Among 108 patients approached by the research assistam, 9 refused to participate and 12 were ineligible. due to confusion (I), deafncss (I). anuripated shalt smy (7). and minimal recent pain (3). Of the remaining 87, 9 were discharged unexpectedly prior to the first follow-up visit by the research assismm and were not included in the analysis. The remaining 78 patients contributed data IO Lhc analpis. Compared to control patients, inrervention patients had lower VAS scores at bascline but slightly higher verbal pain ratings (Table I). There were no differences in the proportion receiving opioid analgesics on admission (Table I).
Main Uulrome Memum There were no significant diierences between groups in the proportion of patients showing imprcnemcnt in self*ported pain from baseline (Table 2). On day 3. intervention group patients were doing worse than control patients in terms of VAS scores but belter in terms of verbal pain scores; control p.~ucnu were 13% more likely 10 show improvement on a summary pain scale TabL
Currem gain, da 3 (N= 78) Wont pan. day 4 (N= 70)
C~“UOl (N = 9s)
(I’? 4, ‘0 ill (‘4, II 4awl.
Univariate disrrihurions of all outcomes measures were first examined to determine the appropriateness of paramerru testing; nonparametric tests were used when data departed markedly from normality. In comparing primary outcomes, differences in Lhc proportion of patients showing improvemcm in pain scale scores on study days 3 and 5 and their 95% confidence intervals were computed using standard methods’s As an ahernative, we also compared mean and median pain scores between groups after subtmcting baseline pain (using r ueslsand Lhe Wilcoxon ranksum test, as appropriate); and we examined the proportion of patients in each group who achieved “adequate” pain control (worst pail1 score C 3) using chisquare 1esl.s.Because the results were substantially the same as for the analysis using “percenr of subjects improved.” we herein report only the latter. Mean sleep duration. sleep latency, .symp tams. and overall quality of life were compared using unpaired I tests. Median opioid analgesia scores were compared using ;he two sample Wilcoxon rank-sum test. Ah statistical analyses were performed using STATA sofrware.”
RoportioDsbarhg-
(N = 40)
53.6 35.0 45.0 2.03 5.68
2
f1omBeselhwinSelf-ReporcedPsin~onF~-UpDqn3nrd5 I”Irrvc”tio”
(40)
Control (%)
37.5 47.5 Verbdpein,dayS(N=78) 40.0 Painsummaty&,&y3(N=78) 575 l-hmupain.day5(N=50) 50.0 Wastpam,day5(N=50) 72.7 V&al+n.&y5(N=50) 45.4 F’aksummaty5cak,day5(N=50) 63.2 Hone dlhr nwnpati wm Wrliukally tignilianl (Pl IlM in ca 11r;ar).
50.0 63.2 31.6 71.0 46.4 85.7 25.0 71.4
Difkrcncc - 12.5 - 15.7 a.4 -15.5 S.6 -13.0 20.4 -3.2
(‘x0)
95% Cl’ ( -34.4.9.0; (-374.6.1) ( - 12.8.29.6) ( - 34.6.75) (-24.3.31.5) ( - 35.7.9.7) ( -5.8.46.7) (-28.9.22.4)
Vd II No. ZFthuq
19%
B&i&
Outcome nlcasurc
Chuling n Pa’n
Inlcwenlion
Average steepthrolrgh first %udyweek (mean hr per night) Awnage skcp hlcncy IhrouRh fin! study week (m&l md, Symptom score through lint study wet (mean) Mem improvement in overall quality of life hetwe& day 3 and baseline Mean improwmenc in 0wr.J quality of life hetwen day 5 and baseline Median ooioii adpesia scolr
(Table 2). On day 5. similar trends were appaten& except that intervention patients were now doing slightly better in terms of VAS current pain (Table 2). None of rhese differences were statisdcally significant. and all were surrounded b Fairly broad confidence intervals. stxondafy-h The intervention had no significant effect on sleep duration or latency, nor on symptom scores or overall quality of life (Table 3). Despite the expectation that a visual pain display might promote more aggressive analgesic dosing, there were no differences in the number of morphine equivalents administered to patients in the two groups, either before adjusting far current verbal pain (data not shown) or after adjustment (Table 3).
The failure of this study to demonstrate the e&ctiveness of the intervention could have several explanations related to the design of the study, the weakness of the intervention, or both. From a design standpoint, this was a small study with limited statistical power to demonstrate significant differences between groups. However, five of eight primary outcomes measures and five of six secondary outcomes measures actually favored the control group. I-I fact, had the sample size been doubled (to Approximately 160 patients) and the point estimates remained the same, several of the results would have favored the control group at conventional levels of statistical significance. The broad conftdence intervals not-
5.26 30.4 2.39 I IO 0.4 I 10.0
(‘~Illrol 5.81 27.5 2.28 0.60 0.96 12.5
DilTcrcncc - 0.55
95% Cl” (-1.41.0.0.31)
2.9
(--5.1.12.0)
0.11 0.50
( -0.26.0.47) (-0.68 1.68)
-055 -2.5
( - 1.74.0.64) D
withstanding. P large positive ovct-all effect of the intervention seems unlikely. Another design issue is that the intervention was intended to affect pain control primarily by influencing physician prescribing behavior. In the two teaching hospitals where the study was conducted, physicians in their first postgraduate year of internal medicine training were responsible for writing all medication orders and were therefore the main target of the intervention. Because some interns cued for both intervention and control-group patients, their exposure to intervention patients might have carried over into their care of control ptients; this would tend to reduce tbe observed benefit of the intervention. On the odder hand, we chose the patient as the unit of anaiysii because the intervention was conceived as feedback regarding the care of one particular patient, not pdtients in general. If anything, this analytical choice should have biased the results toward showing benefit of the intervention. Amdating that the intrrvention had little impact on pain management or outcomes, vdtat could explain its failure? One possibility is chat all patients (including those in the control group) achieved excellent pain control widrin hours of admiin. This seems unlikely became. on day 3.42% of intenention patients and 5L’K of control patients reported V.L.~ “worst pain” scores exceeding 5.0 (P = 0.65). A second possibility is that the experimental intervention itself wds too weak. Owing to budgetary limitations. patients’ Fain reports were charted once daily at most. Charting them several times a dav (as would he the case if nmses
86
Krmitr d OL
assumed thii duty as part of their paucntcare responsibilities) would have resulted in more reliable data, more vivid display of trends, and perhaps greater attention lo patients’ pain status by nurses and physicians. This is one of those rare instances where a real-world intervention (routine charting of pain levels along with vital signs) would actually be strongerand more likely to work-than the experimental model. A third possibility, not incompatible with the second, is that the intervention relied too heavily on the attentiveness, knowledge, and responsiveness of busy house staff. Although house staff received some written materials describing the pain display sheets and were informed about the study during a noon conference on cancer pain management, they received no specific training in how to respond to the information contained on the display sheets. Even though they were alerted IO the patient’s participation in the study by a sticker on the hospital chart, house staff may have barely noticed the graphical displays. let alone responded to them. It may also be that house staff were not sufficiendy educated in the use of the graphic pain display. Anecdotally, the research assistant noted that patients were most likely to receive aggressive pain management when their families helped to transmit their complaints. Enlisting the assistance of family members and mobilizing the nursing staff might have bolstered the intervention. A recent report concluded that daily use of a verbal pain scale can improve the correlation of pain perception between hospitalized oncology patients and their caregivers.’ Physicians and nurses were coached to elicit patients’ pain perceptions themselves; several rounds of coaching were rquired to get clinicians to inquire about pain routinely. The results suggest that getting clinicians actively involved in the process of pain quantification is difFicuh but valuable; simply providing physicians with information (graphical or not) may not be enough. The results of Au et al.” raise two additional questions: whether it is pdde to promote durable changes in physician history-taking behavior, and whether such changes result in better prescribing and improved patient outcomes. As Rubenstein poincl OUI, physician feedback alone in a vari-
VoL II No. ZFebruq
19%
ety of contexts rarely leads to improved patient outcomes: If screening for quality of life deficib is to br effective, it must he linked to comprehemiw. often interdisciplinary assessmentthat determines the causesand sue solutions for the pr0htem.sidentified, followed by imp*mentation of a comprehensivemanagementplao. ‘s In summary, we were unable to show that a simple graphical display improves outcomes for hospitalized patients with cancer-elated pain. Yet given that the quality of c;irr;c~ ka::. :reatment remains unacceptably low.“’ more research is urgently needed. Without doubt, future studies of this type of intervention should enroll more patients (or, like this one, be reported in sufficient detail to allow later meta-analysis). Perhaps more importantly. stronger interventions are needed. This means obtaining patients’ reports several times per day, displaying the data so that they grab physicians’ attention, and incorporating nurses and/or families into the intervention.
Acknowledgment The authon wish to thank the nurses. physicians and patients at the participating institutions for making this study possible. Supported by Grant 4-549860-19990 from the University of California Cancer Research Coordinating Committee.
1. Foley KM. The treatment of cancer pain. N Eng4J Med 19&5:313:&1-94. 2. McCafferyM. Pain control: barriers to the use of available information: World Health Organization Expert Committee on Cancer Pain Relief and Actiw Supportiw Cm. Cancer 1992,7O(suppl5):145B-1449. 3. C&eland CS. Conin It. Hatfield AK. et al. Pain and its treatment in outpatients with mctastaticcancer. N Engl J Med 1994;!330592-596. 4. Jacox A, Cur DB, Payne B, et at. Managemenr of cancer pgin. Clinical practice guideline no. 9. Agency for Health Care Policy and Research (AHCPB) publication no. 94-O!%!. Rockville, MD. AHCPR, US Depenmenr of Health and Human Services.Public Health Service,March 1994. 5. JacoxA,CarrDB.PayncRNewclinicaCpractice guidelines for the mamgement of pain in Patients with -cr. N Fngl J Med 199430651-655.
Vd II No. 2Febmq
1596
lkddr
Charting of Pain
6. Van Roenn JH. Ckeland CS. Conin R. Hatfield AR, Pandp RJ. Phy*dan atthuda and pnctice in callcer pain managcmenc: a 5urwy from the Eacm Gqeratin Oncology Croup. Ann Intern Mcd 1999;119:121-126. 7. Gr-n SA. Sheidkr VR. Swedven K Mucenski J. Piantadosi S. Correlation of patient and caregiver racing of cancer pain. J Pain Symptom Manage 1991;6:53-57. 8. Pozen MW. D’Agortino RB, Selker HP, SytLow&i PA, Hood WB Jr. A predictive instrument to improve coronarytare admission practices in acute ixhcmic heart disease: a prospective multicenter clinical trial. N Engl J Med 1984;310:12731278. 9. DeConno measurement six methods.
F, Caraceni A. Camba A. et al. Pain in cancer patients: a comparison of Pain 1994;57:161-166.
10. Tesa MA, NackleyJE uudks. Annu Rev Public II.
Gill
TM,
Feinslein
Melhod, Health AR.
for quali~f-life 1994:15:X%-559.
A critical
appraisal
of
the quality of qualityof-life 1!394;212:619&?6.
87
measurements.
JA!!
12.
Vkkxcy B. Hays RD. Gaber J, Rauwh R. Engel J, Brook RH. A health-related quahty of life inurumen1 for patients evdluatcd for epilepsy urrgcry. Med care 1992;30:299-319. 13. STATA Corpmation. STATA reference manual: release 3.1.M cd. College Station. TX: STATA Corporation, 1993. 14. Au E. Loprinzi CL Dhodapkar M. et al. Regw lar use O C a veti pain vak impmws dw understanding of onc&gy inpatient pain intensity. J Clw Oncol 1994;12:2751-2755. 15. Rubenstein LV. Using quality of life tests for patient diagnosis or screening. or to mluate ueatmerit. In: Spiker B. ed. Quality of life and ptrarmacoeconomia in clinical trials, 2nd ed. New York: Raw& 1996. pp. 363-374. 16. Bonica JJ. Cancer pain. In: Bonica 1, editor. The management of pain. 2nd cd, vol 1. Philadelphia: Lea and Fcbiger. 1990.
Bedside Charting of Pain Levels in Hospitalized Patients with Cancer: A Randomized Controlled Trial
Znbvductbn Pain is among rhc mos common--and most feared-manifestarions of rancrr. Althwgh precise epidemiologic tiara are iarking. C%t?,90% of adulrs bith progrcsrivc disceasereport substantial pain, and appwximawly one-Ihird
care of cancer-related pain within healthcare institutions is often poor. Many barriers to highquality cancer pain management have been identified, including problems related to ptactitioners (e.g., ignorance of current guidelines; fear of causing addiction), patients (e.g., rcluclance IO report pain and to take pain mcdications). and the health care system (e.g.. inadequate reimtnusemen1 and restrictive regulation of analgesics).’ Inadequate assessment of pain constilutes a further barrier. Failure IO conduct an adequate assessment of pain is recognized as an important reason for undertreatment. Health care providers need IO ask patients ahout pain. because patients are often reluctant IO vohtntrer this information.” Furthermore, clinicians perform poorly when trying to cdmatc patients’ degree of pain without asking them directly.’ A number of reliable and salid pain assessment tools are now available.’ Nevertheless. assessment of patients’ pain will not lead to belter care unless the information is packaged in a way that practitioners can believe it, understand it, and use it. Because undcrstanding is a prerequisite IO appropriate therapeutic action. providing physicians with information on the status of their patients may improve outcomes.” I’hysicians and nurses are accustomed to recording and using clinical information displayed in graphical form. A glance at the vital signs sheer. for example, may tell the physician that the blood pressure is trending down, prompting immediate action. To test tbe hypothesis that visual display of cancer patients’ pain levels might improve their treatment, we conducted a randomized controlled trial. Patients assigned to the intervention group had daily pain assessmentsby study staff who graphically recorded their reported pain intensity on bedside wall charts. Control group patients had pain assessmentsby study staff hut did not have this information displayed. In conducting this trial, we addressed rhrcc principal research questions. First. did the intervention lead IO lower pain intensity during the hospitalization? Second. did the intervention affect patients’ sleep, cancer-related symptoms, or overall quality of life? Third, did the intervention influence physicians to prescribe opioid analgesics more aggressively for their patients with cancer-related pain?
MlZthOdS SPffing
The study tins conducted in early I993 at a large Univ.rsity hospital in southern California and its affiliated VA Medical Center. At both hospitals, patirnts with cancer are admitted both IO general medical services and IO specialized oncology services, and all treatment orders are written by trainees in internal medicine under faculty supervision. Patients wete cligihlz for enrollment in the smdy if :.xy were betwcen 18 and 80 years 01 age, able to speak and undenumd Enghsh. had been adnriued IO the hospital within the past 48 hr with a diagnosis of malignancy, and had at least “moderate pain” during the baseline assessment that was not obviously unrelaled lo cancer or its treatment. Each weekday morning during the study period, a trained research assistant reviewed the medical records of patients admitud during tbe past 24 hr with an admitting diagnosis of cancer (previous 72 hr following a weekend). The research assistant also contacted residents informally after morning report and on the vvarcb IO identify other potentially eligible patients. If medical record review confirmrd that the patient had active cancer, the research assistant approached the patient to assessbaseline pain staurs and request participation in the study. Patients were asked IO indicate their current pain and WOW pain in the past 24 hr using a sliding visual analog device (slide algometer distributed by The Purdue Frederick Company); reported pain could range from 0 IO 10 units. Paticnts with either current pain or worst pain of at least 2.5 units were asked for informed consent and then randomized to either the intervention or control group using a random numbers table. Of 97 eligible patients identified by the research a&tam, 87 consented to participate further (72 at the Univenity, I5 at the VA). However, only 78 patients were available for at least two follow-up pain assessments, and they condtuned the sample for this analysis. Inlervenfh The intervention patients’ self-reported
involved displaying pain levels on a spe-
cially designed sheet that was placed behind patients vital signs on a clipboard in their hospital room. Both muses and physicians were able to review the information during their daily rounds and incorporate the information into their pain management plans. “Current pain” and “worst pain past 24 hours” were charted in red ink on a O-IO scale each day. To highlight trends in symptomatology, dots (current pain) acd x’s (worst pain) were connccted by solid lines. The display sheet also contained space to record patients estimates of the number of hours sleep obtained during the past 24 hr, a short outline of pain management guidelines (in the lower lefthand corner) and an equianalgesic dosing table (in the lower righthand corner). During daily follow-up visits. the research assistant also administered a questionnaire that inquired about quality of sleep, symptoms related to cancer and its therapy. and overall quality of life, but these data were not displayed. Owing to budgetary limitations. pain data were roll -ted only once daily in the morning. The research assis tam asked the same questions of control group patients as intervention group patients, but their responses were not displayed.
MpasuSt?S Main outcomes measures included runrnf pnin [measured on both a continuous visual analogue scale (VAS; range. O-10) and a fivepoint verbal scale spanning mild to excruciating pain (range, l-5)) and -I @zrn in the past 24 hr (measured on a continuous VAS (range, O-10). Previous research has shown these measures to correlate with each other and with the McGill Pain Questionnaire.” Because it was hypothesized that the intervention would take at least 24-48 hr to affect clinician behavior and thereby patients’ pain. we measured current pain and worst pain on the third and fifth day followittg tire initial esahlation (counting the initial valuation as day 1). This allowed time for physicians to note and act upon two to four graphically displayed data points (i.e., from day I to 2 or from days 1 to 4, respectively.) Measurements were ~01 atailable for some patients on days 3 .tnd 5 because of early discharge, weekend or hohday scheddules, or absence from the wards for diagnostic testing. For these patients, we substituted measures taken on day 2 (instead of day 3) or day
4 (instead of day 5). A pain rummar) se& (for days 3 and 5) was formed by multiplying current verbal pain by 2 (making a 2-10 scale), adding the product to the cum of cut rent VAS pain plus VA.5 worst pain, and then dibidmg by 3; the resulting scale could range from 0.67 to 10. WC could not measure day 5 outcomes for 28 patients due to their having been discharged before day 4. Secondary outcomes inrhtded measures of sleep, symptoms, overall quality of life, and narcotic dosing. .Yle+ durnlion was measured as the mean number of houn of sleep reported by the patient through the first week following srudy entry. slnp loferrq was measured as the mean number of minutes taken to fall asleep each night. also as reported by the patient through the first week. A symptomscab scowwas computed as the mean intensity of six symptoms (nausea, vomiting, constipation. loss of appetite, drowsiness. and depression), measured on a I (none) to 6 (very severe) scale and averaged o+er the first week. These symptoms were selected because they are common in cancer patients and may be exacerbated by treatment with opioid analgesics. Cronbach’s alpha reliability for the sixitem symptom scale was 0.55. indicating fair internal consistency of the items. chnall quolify oJl$ was assessed on a daily basis by asking the patient to circle a number on a scale ranging from 0 (WOBL possible quality of life. denoted by a sad face) to 10 (best possible quality of life. denoted by a happy face).“&” Because we expected that making physicians more aware of their patients’ pain might encourage more aggressive analgesic prescrib ing, we computed an +ioid anaigrsia scow in the following way. First. the research a&tam searched pharmacy and n::.sing records to obtain the dose and route of any opioid analgesics administered during the previous 24 hr. Then, we converted the number of milligrams of opioid medication received by patients each day into “paremetal morphine equivalenu” (PMb). using a standard equianalgesic dosing table.’ Sext. to adjust for the amount of pain art individual patient was experiencicg (patients with Ies pain would require less analgesia), we divided that day’s PME tame by the patient’s current verbal pain score (l-5 scale). Finally, we averaged the adjusted PMl3 over study days 24.
lnrenrn~bn
chanc1etiIic Age (mea” yean)
Femsk (96) Non+vhitc(R) Bwline
current pain (mnn viwal analoguc unh)
Lhdine wont pam previou* ?4 hr (mean \iroal andognc units) Bmdine wrhd pain rating (mean) Baselineoverall qualityd-life rating kceking
opioid arulgesiio on admission (‘:o)
Worm-nlth. rompanum~
urn’ u~ltWcaIh signilicrnl
kinsale
48.6 52.6 34.2 2.96 6.51
2.10 5.20 77.5
2.05 4.50 63.1
Among 108 patients approached by the research assistam, 9 refused to participate and 12 were ineligible. due to confusion (I), deafncss (I). anuripated shalt smy (7). and minimal recent pain (3). Of the remaining 87, 9 were discharged unexpectedly prior to the first follow-up visit by the research assismm and were not included in the analysis. The remaining 78 patients contributed data IO Lhc analpis. Compared to control patients, inrervention patients had lower VAS scores at bascline but slightly higher verbal pain ratings (Table I). There were no differences in the proportion receiving opioid analgesics on admission (Table I).
Main Uulrome Memum There were no significant diierences between groups in the proportion of patients showing imprcnemcnt in self*ported pain from baseline (Table 2). On day 3. intervention group patients were doing worse than control patients in terms of VAS scores but belter in terms of verbal pain scores; control p.~ucnu were 13% more likely 10 show improvement on a summary pain scale TabL
Currem gain, da 3 (N= 78) Wont pan. day 4 (N= 70)
C~“UOl (N = 9s)
(I’? 4, ‘0 ill (‘4, II 4awl.
Univariate disrrihurions of all outcomes measures were first examined to determine the appropriateness of paramerru testing; nonparametric tests were used when data departed markedly from normality. In comparing primary outcomes, differences in Lhc proportion of patients showing improvemcm in pain scale scores on study days 3 and 5 and their 95% confidence intervals were computed using standard methods’s As an ahernative, we also compared mean and median pain scores between groups after subtmcting baseline pain (using r ueslsand Lhe Wilcoxon ranksum test, as appropriate); and we examined the proportion of patients in each group who achieved “adequate” pain control (worst pail1 score C 3) using chisquare 1esl.s.Because the results were substantially the same as for the analysis using “percenr of subjects improved.” we herein report only the latter. Mean sleep duration. sleep latency, .symp tams. and overall quality of life were compared using unpaired I tests. Median opioid analgesia scores were compared using ;he two sample Wilcoxon rank-sum test. Ah statistical analyses were performed using STATA sofrware.”
RoportioDsbarhg-
(N = 40)
53.6 35.0 45.0 2.03 5.68
2
f1omBeselhwinSelf-ReporcedPsin~onF~-UpDqn3nrd5 I”Irrvc”tio”
(40)
Control (%)
37.5 47.5 Verbdpein,dayS(N=78) 40.0 Painsummaty&,&y3(N=78) 575 l-hmupain.day5(N=50) 50.0 Wastpam,day5(N=50) 72.7 V&al+n.&y5(N=50) 45.4 F’aksummaty5cak,day5(N=50) 63.2 Hone dlhr nwnpati wm Wrliukally tignilianl (Pl IlM in ca 11r;ar).
50.0 63.2 31.6 71.0 46.4 85.7 25.0 71.4
Difkrcncc - 12.5 - 15.7 a.4 -15.5 S.6 -13.0 20.4 -3.2
(‘x0)
95% Cl’ ( -34.4.9.0; (-374.6.1) ( - 12.8.29.6) ( - 34.6.75) (-24.3.31.5) ( - 35.7.9.7) ( -5.8.46.7) (-28.9.22.4)
Vd II No. ZFthuq
19%
B&i&
Outcome nlcasurc
Chuling n Pa’n
Inlcwenlion
Average steepthrolrgh first %udyweek (mean hr per night) Awnage skcp hlcncy IhrouRh fin! study week (m&l md, Symptom score through lint study wet (mean) Mem improvement in overall quality of life hetwe& day 3 and baseline Mean improwmenc in 0wr.J quality of life hetwen day 5 and baseline Median ooioii adpesia scolr
(Table 2). On day 5. similar trends were appaten& except that intervention patients were now doing slightly better in terms of VAS current pain (Table 2). None of rhese differences were statisdcally significant. and all were surrounded b Fairly broad confidence intervals. stxondafy-h The intervention had no significant effect on sleep duration or latency, nor on symptom scores or overall quality of life (Table 3). Despite the expectation that a visual pain display might promote more aggressive analgesic dosing, there were no differences in the number of morphine equivalents administered to patients in the two groups, either before adjusting far current verbal pain (data not shown) or after adjustment (Table 3).
The failure of this study to demonstrate the e&ctiveness of the intervention could have several explanations related to the design of the study, the weakness of the intervention, or both. From a design standpoint, this was a small study with limited statistical power to demonstrate significant differences between groups. However, five of eight primary outcomes measures and five of six secondary outcomes measures actually favored the control group. I-I fact, had the sample size been doubled (to Approximately 160 patients) and the point estimates remained the same, several of the results would have favored the control group at conventional levels of statistical significance. The broad conftdence intervals not-
5.26 30.4 2.39 I IO 0.4 I 10.0
(‘~Illrol 5.81 27.5 2.28 0.60 0.96 12.5
DilTcrcncc - 0.55
95% Cl” (-1.41.0.0.31)
2.9
(--5.1.12.0)
0.11 0.50
( -0.26.0.47) (-0.68 1.68)
-055 -2.5
( - 1.74.0.64) D
withstanding. P large positive ovct-all effect of the intervention seems unlikely. Another design issue is that the intervention was intended to affect pain control primarily by influencing physician prescribing behavior. In the two teaching hospitals where the study was conducted, physicians in their first postgraduate year of internal medicine training were responsible for writing all medication orders and were therefore the main target of the intervention. Because some interns cued for both intervention and control-group patients, their exposure to intervention patients might have carried over into their care of control ptients; this would tend to reduce tbe observed benefit of the intervention. On the odder hand, we chose the patient as the unit of anaiysii because the intervention was conceived as feedback regarding the care of one particular patient, not pdtients in general. If anything, this analytical choice should have biased the results toward showing benefit of the intervention. Amdating that the intrrvention had little impact on pain management or outcomes, vdtat could explain its failure? One possibility is chat all patients (including those in the control group) achieved excellent pain control widrin hours of admiin. This seems unlikely became. on day 3.42% of intenention patients and 5L’K of control patients reported V.L.~ “worst pain” scores exceeding 5.0 (P = 0.65). A second possibility is that the experimental intervention itself wds too weak. Owing to budgetary limitations. patients’ Fain reports were charted once daily at most. Charting them several times a dav (as would he the case if nmses
86
Krmitr d OL
assumed thii duty as part of their paucntcare responsibilities) would have resulted in more reliable data, more vivid display of trends, and perhaps greater attention lo patients’ pain status by nurses and physicians. This is one of those rare instances where a real-world intervention (routine charting of pain levels along with vital signs) would actually be strongerand more likely to work-than the experimental model. A third possibility, not incompatible with the second, is that the intervention relied too heavily on the attentiveness, knowledge, and responsiveness of busy house staff. Although house staff received some written materials describing the pain display sheets and were informed about the study during a noon conference on cancer pain management, they received no specific training in how to respond to the information contained on the display sheets. Even though they were alerted IO the patient’s participation in the study by a sticker on the hospital chart, house staff may have barely noticed the graphical displays. let alone responded to them. It may also be that house staff were not sufficiendy educated in the use of the graphic pain display. Anecdotally, the research assistant noted that patients were most likely to receive aggressive pain management when their families helped to transmit their complaints. Enlisting the assistance of family members and mobilizing the nursing staff might have bolstered the intervention. A recent report concluded that daily use of a verbal pain scale can improve the correlation of pain perception between hospitalized oncology patients and their caregivers.’ Physicians and nurses were coached to elicit patients’ pain perceptions themselves; several rounds of coaching were rquired to get clinicians to inquire about pain routinely. The results suggest that getting clinicians actively involved in the process of pain quantification is difFicuh but valuable; simply providing physicians with information (graphical or not) may not be enough. The results of Au et al.” raise two additional questions: whether it is pdde to promote durable changes in physician history-taking behavior, and whether such changes result in better prescribing and improved patient outcomes. As Rubenstein poincl OUI, physician feedback alone in a vari-
VoL II No. ZFebruq
19%
ety of contexts rarely leads to improved patient outcomes: If screening for quality of life deficib is to br effective, it must he linked to comprehemiw. often interdisciplinary assessmentthat determines the causesand sue solutions for the pr0htem.sidentified, followed by imp*mentation of a comprehensivemanagementplao. ‘s In summary, we were unable to show that a simple graphical display improves outcomes for hospitalized patients with cancer-elated pain. Yet given that the quality of c;irr;c~ ka::. :reatment remains unacceptably low.“’ more research is urgently needed. Without doubt, future studies of this type of intervention should enroll more patients (or, like this one, be reported in sufficient detail to allow later meta-analysis). Perhaps more importantly. stronger interventions are needed. This means obtaining patients’ reports several times per day, displaying the data so that they grab physicians’ attention, and incorporating nurses and/or families into the intervention.
Acknowledgment The authon wish to thank the nurses. physicians and patients at the participating institutions for making this study possible. Supported by Grant 4-549860-19990 from the University of California Cancer Research Coordinating Committee.
1. Foley KM. The treatment of cancer pain. N Eng4J Med 19&5:313:&1-94. 2. McCafferyM. Pain control: barriers to the use of available information: World Health Organization Expert Committee on Cancer Pain Relief and Actiw Supportiw Cm. Cancer 1992,7O(suppl5):145B-1449. 3. C&eland CS. Conin It. Hatfield AK. et al. Pain and its treatment in outpatients with mctastaticcancer. N Engl J Med 1994;!330592-596. 4. Jacox A, Cur DB, Payne B, et at. Managemenr of cancer pgin. Clinical practice guideline no. 9. Agency for Health Care Policy and Research (AHCPB) publication no. 94-O!%!. Rockville, MD. AHCPR, US Depenmenr of Health and Human Services.Public Health Service,March 1994. 5. JacoxA,CarrDB.PayncRNewclinicaCpractice guidelines for the mamgement of pain in Patients with -cr. N Fngl J Med 199430651-655.
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1596
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Charting of Pain
6. Van Roenn JH. Ckeland CS. Conin R. Hatfield AR, Pandp RJ. Phy*dan atthuda and pnctice in callcer pain managcmenc: a 5urwy from the Eacm Gqeratin Oncology Croup. Ann Intern Mcd 1999;119:121-126. 7. Gr-n SA. Sheidkr VR. Swedven K Mucenski J. Piantadosi S. Correlation of patient and caregiver racing of cancer pain. J Pain Symptom Manage 1991;6:53-57. 8. Pozen MW. D’Agortino RB, Selker HP, SytLow&i PA, Hood WB Jr. A predictive instrument to improve coronarytare admission practices in acute ixhcmic heart disease: a prospective multicenter clinical trial. N Engl J Med 1984;310:12731278. 9. DeConno measurement six methods.
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appraisal
of
the quality of qualityof-life 1!394;212:619&?6.
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measurements.
JA!!
12.
Vkkxcy B. Hays RD. Gaber J, Rauwh R. Engel J, Brook RH. A health-related quahty of life inurumen1 for patients evdluatcd for epilepsy urrgcry. Med care 1992;30:299-319. 13. STATA Corpmation. STATA reference manual: release 3.1.M cd. College Station. TX: STATA Corporation, 1993. 14. Au E. Loprinzi CL Dhodapkar M. et al. Regw lar use O C a veti pain vak impmws dw understanding of onc&gy inpatient pain intensity. J Clw Oncol 1994;12:2751-2755. 15. Rubenstein LV. Using quality of life tests for patient diagnosis or screening. or to mluate ueatmerit. In: Spiker B. ed. Quality of life and ptrarmacoeconomia in clinical trials, 2nd ed. New York: Raw& 1996. pp. 363-374. 16. Bonica JJ. Cancer pain. In: Bonica 1, editor. The management of pain. 2nd cd, vol 1. Philadelphia: Lea and Fcbiger. 1990.