AN AUTO-ENCODING SYSTEM FOR ADVERSE REACTIONS. USING THE WHO DICTIONARY

Transcription

1 Pharmaceutical 1 59 AN AUTO-ENCODING SYSTEM FOR ADVERSE REACTIONS. USING THE WHO DICTIONARY Denis Michel Janssen Research Foundation INTRODUCTION A manual system in which a data reviewer enters the WHO codes 011to case report forms for adverse reactions is part of the clinical data flow at many pbannaceutical companies. Tbe objective of the auto-encoding system is to automate the process so that only the investigator term is entered into the database, and the WHO code is automatically selected by the system. Because investigators are encouraged to describe adverse experiences in their own words. a simple merge of investigator terms with WHO included terms is insufficient. SAS software bas excelled! text bandling capabilities wbicb are used to manipulate the investigator terms without changing their meanings. This increases the matcb ratio of coded terms significantly. FILES USED BY THE SYSTEM Tbe WHO dictionary is stored in a sequential file containing the term and the code in fixed columns. WHO preferred and iocluded terms are maintained in a single file. Tbe tenn occupies 40 columns and the code contains seven digits. Tbe first four digits identify the preferred term. Tbe preferred term bas '001' as the last three digits. Included terms begin with '002'. This file serves as the input control data set for the PROC FORMAT used to generate the WHO format. In addition to tbe WHO dictionary, three other sequential files used by the system are maintained by an individual. An abbreviations file contains words frequently abbreviated with their expanded meanings in specified columns. A sample reccml in tbis file contains the abbreviation "inc" with the expanded tenn "increased". A word deletion file contains words which are found in investigator terms wbicb often prevent a matcb, but which do not add meaning to the term. Sample records in tbis file cootain tbe words "a" "the" "of' "in" "at", etc. Tbe third sequential file is the added terms file. This file, structured identically to the WHO dictionary file, contains terms not found in the WHO dictionary with their associated WHO codes. A sample record in this file contains the term "headaches" with the WHO code " ", which bas tbe formatted value "headache", a WHO preferred term. A SAS data library containing three SAS data sets is maintained as part of the system. Tbe first data set contains all of the unique WHO and added terms with their WHO codes. A data set containing one observation with all abbreviations and their expanded meanings is maintained as well as a data set with one observation containing the words from the wcml deletion file. MAINTENANCE OF THE ABBREVIATIONS AND WORD DELETION SAS DATA SETS Tbe abbm'iations file is read inlo a SAS data set and transposed to form a single observation with an array of abbreviations and an array of expanded meanings. 1be dimension of the array is also stared as a variable in tbe pennanent SAS data set. *** UPDATE 1HB ABBREVIATION DATA SBT; libname auto 'auto.encode.adnasdb' disp-old; filename abbrev 'auto.encode.dict(autoabb)'; data temp; infile abbrev misaover; abbrev Bxpand $20.; proc sort dataaotemp; by abbrev; data temp; set temp(wbere-(abbrev ne ' ')) end-eof; by abbrev; if last.abbrev; length couu 3; count+ 1;

2 1 6 0 Pharmaceutical if eof then call symput('index',left(put(count,3.))); nm; pmc datasers library=auto; delete abbrev; transpose to 1 obs and write pennanent data set: data auto.abbrev; set temp end-eof; array _abb {&index) $10 abb1-abb&:index; array _exp {&index) $20 expl-exp&index; retain abb 1-abb&index exp 1-exp&index; _abb( count }-abbrev; _exp( count )-expand; if eof then output; drop abbrev expand; The word deletion file is processed in the same way as the abbreviations file to create the word deletion pennanent SAS data sel abbreviations file, or lhe word deletioo file is modified. For each duplicate tenn deleted, a message is written to the SAS log. The resulting permanent SAS data set contains unique terms with their WHO codes and is used to match to modified investigator terms. AUTO-ENCODING INVESTIGATOR TERMS The auto-encode system is submitted by the end user for the codiog of investigator terms. The only specification requiied is the code used to identify the study on disk. The uncoded investigator terms are manipulated in the same S step process as previously described for the processing of the WHO dictionary. Then a match is searched for in the modified dictionary. The matched tenns are coded and two reports are issued, one report of coded terms for review and a report of uncoded investigator terms. REMOVING PUNCTUATION AND NUMBERS PROCESSING THE WHO DICTIONARY The tenns in the WHO dictionary and the added tenns file are coocatenated and read into a SAS data set for processing. The processing includes the following: 1. Remove case sensitivity (upcase function). 2. Remove punctuation and nwnbeb (IIliiiSlate and compress functions). 3. Expand abbreviations (SAS macro). 4. Delete unoec::essary words (SAS macro). 5. Remove the importance of the order of individual words in the tenn (SAS macro). After the processing, the SAS data set will contain duplicates. Tbis is due primarily to the removal of word order in the WHO dictionary. For example, "unexpected therapeutic effect" with preferred code contains the same words as "therapeutic effect unexpected" with included code Since only unique modified tenns are stored in the SAS data set, a hierarchy of precedence was established WHO terms bave priority over added temis, and preferred tenns have priority over included tenns. In the case above, investigator terms are coded A SAS program is submitted to process the dictionary whenever the WHO dictionary, added tenns file, The translate function is used to replace most punctuation and numbers by a blllllt space. The compess function is used for periods and single quotes. The compress function prevents breaking up abbreviations and words containing apostrophes. EXPANDING ABBREVIATIONS The macro below is used to expand abbreviations in investigator terms. u oonn u u u-.... Macro : _expand(expa!ld abbreviations in temt) instruct: required keywords: dsn data set name vamame variable name maxword maximum words in term (default=io)..., %macro _expa!ld(dsn=, vardllllle-, maxwoid lo); % find number of abbreviatioos; data abbrev(dropecount); set auto.abbrev; call symput('count' Jeft(pul(count,3.)));

3 Pharmaceutical 1 61 data &dso; set &dso; if _n_ 1 tbeo set abbrev; array _abb I *I $ abbl-abb&c:ouot; array _exp I *I $ expl-exp&c:ouot; array _words 1*1 $40 word1-word&maxword; break term into words; length temp $40; tempooleft(&:vamame ); do I 1 to dim(_ words); pos-indexc(temp,' '); ifpos>1 then do; _words(i} substr(temp,l,pos-1); temp=trim(left(substr(temp,pos+ 1)) ); else _words(i} =temp; expand abbreviations; j 1; do while(_words(j} ne ' '); do k = 1 to dim(_abb); if _words(j} = _abb{kl then _wordslj}., _exp(k}; j. j+l; drop i j k pos abbl-abb&c:ouot exp1-exp&c:ount temp; %mend _expand; DELETING UNNECESSARY WORDS 1be macro below is used to delete words in tbe word deletion file from investigator teims. data &:dso; set &:dso; if _n_ l tben set worddel: array _del(*} $ dell-del&:count; array _words (*I $ wordl-word&maxword; delete the words in tbe word deletion data set; j,.,l; do while(_words{i} ne ' '); do j = 1 to dim(_del); if _words(i} _del(jl then _words(i}=' '; i- i+l; drop i j dell - del&:counl: %*reassemble term; data &:dsn; set &:dsn; length aeold $40; array _words(*} wordl - word&maxword; aeold=' ': do i 1 to dim(_ words); if_ wordsli I oe ' ' then do; aenew = left (trim (left (aeold)) ]] ' ' ]] trim (left(_wordsli}))); aeold-aeoew; drop aeold I wordl - word&maxword; %mend _delwrd; uuuua uu u n n uu u euo o Maao : _delwnl (delete words in leim matching word delete data set) idstruct: requiled keywords: dsn dala set name maxword = maximum words in tenn * (default=lo) -requiles variables wordl-word&maxword... t REMOVING THE IMPORTANCE OF WORD ORDER (THE WORD SORTER) 1be individual words id tbe investigator term are sorted alphabetically to aeate a new term id wbicb word order is unimportant. A word sorting macro performs tbis task. %macro _delwrd(dmc, maxword lo); %* find number of words to be deleted; data worddel(drop-count); set auto.worddel; call symput( 'count',left(put( couot,3.)) );

4 1 6 2 Pharmaceutical.. OOOOUUOOOOOOOOOOOOOOOOOOUOOOOOOOOUOOUOOOOOOOOOOUOOOOOOOOOUoooooooononooouoonooono Macro : wordsn (son words in text string variable) instruct: requiml keywords: vamame variable name of teml vuson = variable name of sorted tenn exclude tenn excluded from sorting delim =delimiter (default=' ') varlen length of variable (defaultoo40) maxword = maximum words in tenn (default=lo)..., %Macro wordsn(vamame, varson=, exclude-, delim ' ', varlen=40, maxword lo); % break tenn into words and son alphabetically; length &varson tempword wordl-word&maxword $&varlen: drop tempword wordnum wrdcount wordi word&maxword i j; amy _words 1 1 word! - word&maxword; &varson = &vamame: wordnum l; tempword.. scan(&varson. wordnum, &delim); wrdcount = 0; do wbile(tempword"==' '): i = 1: do wbile(i < wrdcount); if _wordsli} > tempword then do; do j = wrdcount to i by -1: _wordslj+ll _wordslj J; _words{i} = tempword; wrdcount wrdcount+l: i wrdcount+loo; i=-i+i; if i = wrdcount + I then do; wrdcount = wrdcount+i; _words{wrdcount} = tempword; wordnum = wordnum + 1; tempword = scan(&varson, wordnum, &delim); %* assemble tenn from sorted woids: %if %length(&exclude) 11., 0 %tben %str(if &vanon ne &exclude then do;); tempword ' '; do I = I to wrdcount; &vuson.. left( ttim( left( tempword)) ]] ' ' ]] trim (left Lwords(il))); tempword-&vanon; %if %length(&exclude)ii-o %then %str(); %mend wordsn; REPORTS The repons issued are soned tim by investiga~or tem~ and then by patient number. 1bis allows the dal8 reviewer to code terms grouped toaetber, idstead of coding all terms for a patient case report fcmn, as in a manual system. The advantage in terms of consistency is more imponant than the time advantage of Ollly baving to code specific tenns once. The coded terms repon is for review only. There are various actions for tbe data reviewer to take for the uncoded terms. Any spelling errors are corrected in investigator terms. Terms tbat are common across studies are good candidates for tbe added terms file. If these actions are taken, anotber execution of tbe auto-encode program will code the terms. Tbe remaining terms are coded manually. MATCH RATIO Tbe percemage of investillfor tenns tbat are automllically coded varies among studies and investigators. If tbe users establish a hqe added terms file, tbe mllcb ralio will increase sisnificantly. 1bis incre~~e in the malcb ratio is partially offllet by some degradation in performance. Laboratory abnonnalities in wbicb tbe teml contains the test name and result will require manual coding. In some cases, the case report form will bave to be reviewed prior to assigning a WHO code to a tenn.

5 Pharmaceutical AN EXAMPLE OF AUTO-ENCODING A TERM 1be term entered onto the case report fonn by the investigator is: within the first 16 characters. While this may work for certain dictionaries, such as drug dictionaries, 16 cbaracters is insufficient to describe a unique WHO adverse reaction tenn. "Inc. Blood Pressure (145/100 at 11:00)". 1. Remove case sensitivity: INC. BLOOD PRESSURE (145/100 AT 11:00) 2. Remove punctuation and numbers: INC BLOOD PRESSURE AT SAS is a registered trademadt of SAS Institute Inc., Cary, NC, USA. 3. Expand abbreviations: INCREASED BLOOD PRESSURE AT 4. Delete unnecessary words: INCREASED BLOOD PRESSURE 5. Sort the words: BLOOD INCREASED PRESSURE 1be dictionary is searched for the modified tenn. A match is found at modified included term "BLOOD INCREASED PRESSURE" with code be included tenn is. increased blood pmswe and the preferred tenn for safety tabulation is hypertension, with WHO code CONCLUSION An auto-encode procedure for adverse reactions is a necessary tool in a clinical data processing eovironment. 1be decrease in time from case report fonn retrieval to safety analysis and the increase in consistency across studies and across data reviewers justifies the programming time required. A simple system of coding terms that exactly match WHO terms can be improved upon by SAS programming. While the System described above was developed for the WHO dictionary, the SatDe method can be applied to another dictionary, such as COSTART. Version 6 of SAS offers tbe ability to create user-defined infonnats usiog the FORMAT procedure. Since matching tenns using the input function is more efficient than merging the tenns, the creation of an infonnat using the modified WHO terms as an input control data set would fit well into an auto-encoding system. Unfortunately, the value read with a user-defided infonnat must be unique