ããèäžåºè¡ã¯è³è¡ç®¡é害ã®äžã§ãäºåŸäºæž¬ãé£ãããšèšãããŠããçŸæ£ã®äžã€ã§ããä»åãããèäžåºè¡ã®äºåŸäºæž¬ã«ã€ããŠãæ©èœäºåŸãADLã®äºåŸãäºæž¬ããããã«å¿ èŠãªããšããŸãšããŠãããããšæããŸãã
ç®æ¬¡
- 1 ããèäžåºè¡ã®äºåŸäºæž¬ïŒæ©èœäºåŸãADLã®äºåŸãäºæž¬ããããã«å¿
èŠãªããšïŒ
- 1.1 ããèäžåºè¡ãäºåŸäºæž¬ã«ã€ããŠã®ããããèšäº
- 1.2 ããèäžåºè¡ã®äºåŸäºæž¬ã¯é£ããïŒ
- 1.3 ããèäžåºè¡ã®äºåŸäºæž¬ã«å¿ èŠãªãã®ã¯ïŒ
- 1.4 幎霢ãFisheråé¡ãæèéå®³ã®æç¶æéãçšããããèäžåºè¡ã®äºåŸäºæž¬ïŒADLïŒ
- 1.5 åŒåžçæ³èªå®å£«ã®è³æ Œãåãããæ¹ã¯å¿ èŠ
- 1.6 ã¹ããæéå匷ãªããªããã¡
- 1.7 PTOTSTãä»ãã絊æãäžããå ·äœçæ¹æ³
lineç»é²ããããããé¡ãããŸã
ããã°ã«ã¯æžããªãè£è©±ãæŽæ°éç¥ãåã ã¡é宿 å ±ãªã©ãé ä¿¡(å®å šç¡æ)ïŒãŸãã¯åã ã¡è¿œå ãâª
èšåºãå©ããnote
çæ³å£«ã§å°æ¥ã®ãéãå¿é ãªæ¹ãžâšãªãããªããŒã·ã§ã³è·çš®ã®ããã®è³ç£åœ¢æè¡-äœæ¥çæ³å£«ã®çè ãå®éã«è¡ãè³ç£éçšæ³ãå®ããŒã¿ãå ã«ç޹ä»-
蚪åæå°ã§ãæ©ã¿ã®æ¹ãèªä¿¡ããªãæ¹
âšèšªåæå°ã§åšå® 埩垰ãšäœå® æ¹ä¿®ãæåãããã³ã
åé èé害ã«å¯ŸãããªãããªããŒã·ã§ã³
âšéè¡æ©èœé害ãªãïŒGMTãèªå·±æç€ºæ³ãåé¡è§£æ±ºèšç·ŽãTPMïŒ
泚æèª²é¡ã®ããªã³ã課é¡
âšæ³šæé害ããªã³ã課é¡ããŒã¿ïŒæåéžæãèšç®ãå³åœ¢ïŒ
æ©æå·ã®ãªãããªããŒã·ã§ã³
âšæ©æå·ã®ãªãããªããŒã·ã§ã³ïŒè³ç»åããã®è©äŸ¡é ç®éžå®ãæ²»çæŠç¥ç«æ¡ïŒ
è³ç»åã®é人ãž
âšæ°äººã»åŠçãããè³ç»åã®é人ã«è¿ã¥ãããã«ïŒè³éšäœãšæ©èœå±åšãè³ã®ã€ãªããããèããç»åã®èšºæ¹ïŒ
ãªãããªããŒã·ã§ã³ãšéååŠç¿
âšãªãããªããŒã·ã§ã³ãšéååŠç¿ïŒä¿æãè»¢ç§»ïŒæ±åïŒãä¿ãæ¹æ³ïŒ
èªç¥çãªãããªããŒã·ã§ã³
âšèªç¥çã«ãããäœæ¥æŽ»åã®éèŠæ§ãšèª²é¡èšå®ãè©äŸ¡ã®æ¯æŽãšãã€ã³ããè³æ©èœé¢ãèæ ®ããŠææ¬²ãšéååŠç¿ãä¿ããADLã»IADLãä¿ãæ¹æ³ã
èµ·ç«ãšç座åäœã®ãªãããªããŒã·ã§ã³
âšèµ·ç«ãšç座åäœãäžæããããªãã®åå åæã誰ã§ãçè§£ã§ããçæŽ»åãšãã€ãªã¡ã«ãã¯ã¹ãè³æ©èœãšã®é¢é£æ§ãèžãŸããªããã
æèŠé害ã®ãªãããªããŒã·ã§ã³
âšæèŠé害ã®ãªãããªããŒã·ã§ã³ïŒè³ç§åŠãšäŒçµ±çãªããèåãããèãæ¹ãšå®è·µæ¹æ³ãéææ§ã®ä¿é²ãèŠæ®ããŠãïŒ
ããèäžåºè¡ã®äºåŸäºæž¬ïŒæ©èœäºåŸãADLã®äºåŸãäºæž¬ããããã«å¿ èŠãªããšïŒ
ã¹ãã³ãµãŒããµãŒã
ããèäžåºè¡ãäºåŸäºæž¬ã«ã€ããŠã®ããããèšäº
- è³åäžç麻çºã®äºåŸäºæž¬ïŒæ¥æ§æãäžè¢ãæ©è¡ã倱èªïŒã®æ¹æ³ïŒ
- ãªãããªã«åœ¹ç«ã€è³ç»åïŒè³éšäœãšæ©èœå±åšãè³ã®ã€ãªããããèããç»åã®èšºæ¹ïŒ
- è³è¡ç®¡é害ãšãªãããªããŒã·ã§ã³ã«ããããªã¹ã¯ç®¡çã®ãã€ã³ãïŒ
- ããèäžåºè¡ã®ç æ çè§£-äºåŸã«åœ±é¿ãäžããè³è¡ç®¡æ£çž®-
- ããèäžåºè¡ã®ç æ çè§£-äºåŸã«åœ±é¿ãäžããè³è¡ç®¡æ£çž®-
- ããèäžåºè¡ã®ç æ çè§£-äºåŸãå·Šå³ãã埪ç°è¡æ¶²éã®ã¢ã»ã¹ã¡ã³ã-
- ãªã¹ã¯ç®¡çã«æŽ»ããè³ãã«ãã¢ãš midline shiftã®é¢ä¿æ§-é éšCTç»åã®ç¹åŸŽ-
- è³è¡ç®¡çŸæ£ãšãªã¹ã¯ç®¡ç-è³è¡æµéã®èãæ¹-
ã¹ãã³ãµãŒããµãŒã
ããèäžåºè¡ã®äºåŸäºæž¬ã¯é£ããïŒ
ããèäžåºè¡ã®äºåŸäºæž¬ã¯é£ãããšãããŠããçç±ãšããŠã¯ããã®çç¶ãçµéãå€åœ©ãªããšã«ãããŸãã
ããèäžåºè¡ã§ã¯æèé害ãé·åŒããããæè¡ãæ°Žé çãªã©ã®å䜵çã«ããå埩ãé£ãããªãããšãå€ããããŸãã
ãŸããéã®ãã¿ãŒã³ãšããŠæ¥æ¿ã«å埩ãèŠãããäŸããã£ããããŸãã
ãã®ãããªçç±ãããããèäžåºè¡ã®äºåŸäºæž¬ã¯é£ãããšãããŠããŸãã
ã¹ãã³ãµãŒããµãŒã
ããèäžåºè¡ã®äºåŸäºæž¬ã«å¿ èŠãªãã®ã¯ïŒ
ããèäžåºè¡ã®äºåŸäºæž¬ã«å¿
èŠãªãã®ïŒææïŒãšããŠã¯ä»¥äžã®ãããªãã®ããããŸãã
ä»åã¯ãå®®è¶å
çã®ãããèäžåºè¡ã«ãããŠé颿ADLã«åœ±é¿ãäžããå åã®æ€èšâClassification and regression treesïŒCARTïŒãçšããäºåŸäºæž¬ã®è©Šã¿âããåèã«ããŠãããŸãã
ã»å¹Žéœ¢
ã»Fisheråé¡
ã»æèéå®³ã®æç¶æé
Fisheråé¡
Groupã1
è¡æ¶²ã®èªããããªããã®
Groupã2
ã³ãŸãæ§ãŸãã¯åçŽã®è³æ§œã«1mm æªæºã®è¡æ¶²å±€
Groupã3
屿çãªè¡è
«ãããã¯è³æ§œã«1mm 以äžã®è¡æ¶²å±€
Groupã4
ã³ãŸãæ§ããèäžåºè¡ãŸãã¯ããèäžåºè¡ããªããŠããè³å
ãŸãã¯è³å®€å
ã«è¡è
«
Fisher group1-4ã¯ç¹æ°ã倧ããã»ã©éçã«ã¯ãªã£ãŠãããïŒgroup1-3ã¯ããèäžåºè¡ã®çšåºŠã«æ¯äŸããŠç¹æ°ãå¢ããŠããŸããïŒFisher group4ã¯ããèäžåºè¡ããªããéåžžã«èãïŒéåžžã¯è³å åºè¡ãè³å®€å åºè¡ã ããããããšã瀺ããŠããŸãïŒ
åºæ¬çã«ããèäžåºè¡ãããã°ïŒè³å åºè¡ãè³å®€å åºè¡ã®æç¡ã«ããããã Fisher group1-3ã®ã©ããã«ãªããŸãïŒ
Fisherã®åå žã®è¶£æšã¯ïŒé床ã®ããèäžåºè¡ã«çåæ§ã®è³è¡ç®¡æ£çž®ã¯èµ·ãããïŒè³å åºè¡ãè³å®€å åºè¡ã ãã§ ã¯è³è¡ç®¡æ£çž®ã¯èµ·ãããªãïŒèšãæãããšFisher group3ã®ã¿ã«çåæ§ã®è³è¡ç®¡æ£çž®ãèµ·ããããšã§ãã.å°å®®å±±ä»ãFisher group 4 ã®èª€è§£ãè³åäžã®å€ç§ 43: 232 ã 233ïŒ201
ã¹ãã³ãµãŒããµãŒã
æèéå®³ã®æç¶æé
JCSïŒJapan Coma ScaleïŒ
â
åºæ¿ããªããŠãèŠéããŠããç¶æ
ïŒâæ¡ã§è¡šçŸïŒ1 å€§äœæèæž
æã ããä»ã²ãšã€ã¯ã£ããbãªã 2 èŠåœèé害ããã 3 èªåã®ååãçå¹Žææ¥ããããªã |
â
¡ åºæ¿ãããšèŠéããç¶æ
âåºæ¿ãããããšç ã蟌ãâïŒïŒæ¡ã§è¡šçŸïŒ10æ®éã®åŒã³ããã§å®¹æã«éçŒãã ïŒåç®ççãªéåããããèšèãã§ããééããå€ãïŒ 20倧ããªå£°ããŸãã¯äœãæºãã¶ãããšã«ããéçŒãã ïŒç°¡åãªåœä»€ã«å¿ãããäŸãã°é¢æ¡æïŒ 30çã¿åºæ¿ãå ãã€ã€ãåŒã³ãããç¹°ãè¿ããšãããããŠéçŒãã |
â
¢ãåºæ¿ããŠãèŠéããªãç¶æ
ïŒïŒæ¡ã§è¡šçŸïŒ 100çã¿åºæ¿ã«å¯Ÿããã¯ããã®ãããããªåäœããã 200çã¿åºæ¿ã§æè¶³ãåãããããé¡ããããã 300çã¿åºæ¿ã«åå¿ããªã |
泚ãR ïŒäžç©ãI: 倱çŠãA ïŒç¡åæ§ç¡èšã 倱å€å¥çå矀
ãã®äžã§ãïŒæ¡ä»¥äžã®æèéå®³ã®æç¶æéãå åãšããŠããŸãã
æèé害ã®è©äŸ¡ã«ã€ããŠã¯ä»¥äžã®èšäºãåç
§ããŠãã ããã
æèé害ïŒããåŠå«ãïŒã®ã¡ã«ããºã ãšè©äŸ¡æ¹æ³ããªãããªããŒã·ã§ã³ã¢ãããŒãïŒ
ã¹ãã³ãµãŒããµãŒã
幎霢ãFisheråé¡ãæèéå®³ã®æç¶æéãçšããããèäžåºè¡ã®äºåŸäºæž¬ïŒADLïŒ
åéããããããèäžåºè¡ã«ãããŠé颿ADLã«åœ±é¿ãäžããå åã®æ€èšâClassification and regression treesïŒCARTïŒãçšããäºåŸäºæž¬ã®è©Šã¿âãã«ãããŠã幎霢ãFisheråé¡ãæèéå®³ã®æç¶æéãçšããããèäžåºè¡ã®äºåŸäºæž¬ã玹ä»ãããŠããŸãã
ããã«ãããšã
63æ³ä»¥äžã®ADLéèªç«äºæ³çŸ€ïŒ85.7ïŒ
62æ³ä»¥äžã§Fisheråé¡2以äžã®ADLèªç«äºæ³çŸ€ïŒ83.3ïŒ
å
šäœïŒ77.4ïŒ
ã§äºæž¬ãçäžããŠãããšã®ããšã§ãã
ADLã®èªç«ãéèªç«ã«ã€ããŠã¯ãèªç«ãFIM116ç¹ä»¥äžãéèªç«ãFIM115ç¹ä»¥äžãšããŠããŸãã
å³ãèŠãŠããããšã幎霢ã63æ³ä»¥äžã®æ¹ã§ã¯ADLã«äœããã®ä»å©ãå¿
èŠã§ããã®ã85.7%ãšããäºæž¬ã«ãªããŸãã
62æ³ä»¥äžã®æ¹ã§ã¯ãFisheråé¡ã2以äžã§ããã°ADLãèªç«ããã®ã88.3%ãšããäºæž¬ã«ãªããŸãã
ãŸãã62æ³ä»¥äžã®æ¹ã§ãFisheråé¡ã3以äžã§ããã°ã
ã»JCSïŒJapan Coma ScaleïŒã®ïŒæ¡ä»¥äžã®æèéå®³ã®æç¶æéã6æ¥é以äžã§ããã°ADLãèªç«ããã®ã66.7%ãšããäºæž¬ã«ãªããŸãã
ã»JCSïŒJapan Coma ScaleïŒã®ïŒæ¡ä»¥äžã®æèéå®³ã®æç¶æéã7æ¥é以äžã§ããã°ADLã«äœããã®ä»å©ãå¿
èŠã§ããã®ã60.0%ãšããäºæž¬ã«ãªããŸãã
åŒåžçæ³èªå®å£«ã®è³æ Œãåãããæ¹ã¯å¿ èŠ
åŒåžçæ³èªå®å£«ã®è³æ Œå匷ã¯ééæéã«ããã®ãã³ãã§ããåŒåžçæ³èªå®å£« eã©ãŒãã³ã°è¬åº§

ã¹ããæéå匷ãªããªããã¡
PTOTSTã®ããã®ã»ãããŒåç»ãèŠãããŸããååéã®ã¹ãã·ã£ãªã¹ããç»å£ããŠããã®ã§ãææ°ã®ç¥èŠãåŠã³ãªããèšåºã«å³æŽ»ããäºãå¯èœã§ãã
ã»ãããŒããããã§ãããããã¡ã¢åãã«å€¢äžã«ãªãèãéããŠããŸã£ãã
ãªããŠããšã¯ãªããªããŸããäœåºŠã§ãèŠè¿ãäºãå¯èœã ããã§ãã
é«é¡ãªã»ãããŒæïŒäº€éè²»ãæŒé£ä»£ãæ¯æãããããã¹ããæéãèŠã€ããŠå匷ã§ããããªããã¡ãã詊ããŠã¿ãã®ãè¯ãã®ã§ã¯ãªãããšæããŸãã
èšåºã§å·®ãã€ãã人ã¯çé ããŠåªåããŠããŸããã
é·ãæéã§å¥çŽããã»ãããæé¡ãå®ããªããŸãã
PT.OT.STã®ããã®ç·åãªã³ã©ã€ã³ã»ãããŒããªããã¡ã

PTOTSTãä»ãã絊æãäžããå ·äœçæ¹æ³
転è·ãµã€ãå©çšã®ã¡ãªãã
äœããã®çç±ã§è»¢è·ããèãã®æ¹ã«ã管ç人ã®çµéšãå ã«è»¢è·ãµã€ãã®å©çšã®ã¡ãªããã説æããŸããè»¢è·æŽ»åãããäžã§ã倧å€ãªããšãšããŠããã
ä»äºãããªããè»¢è·æŽ»åïŒæ±äººæ å ±ïŒãæ¢ãã®ã¯æéãããã
ãã®äžç¹ã«éçŽãããã®ã§ã¯ãªãã§ããããïŒïŒä»ã«ããããããããŸãããïŒ
管ç人ã¯è»¢è·ãµã€ããå©çšããŠçŸåšã®è·å Žã«è»¢è·ããŸããã
ã³ãŒãã£ããŒã¿ãŒã®æ¹ãšã¯äž»ã«é»è©±ãLINEãéããŠã®ã³ãã¥ãã±ãŒã·ã§ã³ãäžå¿ãšããŠèªåã®æ±ããæ¡ä»¶ã«åãæ±äººæ å ±ãæ¢ããŠããããŸããã
æ¥ã èšåºæ¥åãããªããªãããããœã³ã³ãã¹ããã§æ±äººæ å ±ãæ¢ããšããã®ã¯æéã§ãããç²ããŸãã
ããããæå³ã§ã¯ã転è·ãµã€ãå©çšã®ã¡ãªããã¯å€§ãããšèããŠããŸãã
転è·ãµã€ãå©çšã®ãã¡ãªãã
ãã¡ãªãããšããŠã¯ã転è·ãµã€ããéããŠè»¢è·ãããšã転è·å ã®ç é¢ãæœèšã¯ç޹仿ïŒè»¢è·è ã®å¹Žåã®20-30%ïŒãæ¯æãããšã§ããããããªããã¡ãªããããšãããšãè»¢è·æã®çµŠäžäº€æžã«ãããŠã絊äžãäžãã«ãããšããããšã«ç¹ãããŸãã
ããã§ããç é¢ãæœèšåŽã欲ãããšæãã人æã§ããå Žåã絊äžäº€æžã¯è¡ãããããªãã¯ãã§ãã
ãããã£ãæå³ã§ãã玹ä»ããŠããã£ãç é¢ãæœèšã®ãªãããªç§ãã©ã®ãããªçŸç¶ã§ãã©ã®ãããªäººæã欲ããã®ããšãã£ãæ å ±ããèªåã®æã€åŒ·ã¿ã掻ãããããšãã£ãèŠç¹ã§è»¢è·æŽ»åãé²ããŠããããšã倧åã«ãªããŸãã
転è·ãµã€ãã¯è€æ°ç»é²ããããšãå¿ èŠ
転è·ãµã€ãã¯è€æ°ç»é²ããŠããããšãéèŠã«ãªããããããŸãããããã¯ã転è·ãµã€ãã«ãã£ãŠæ±äººæ å ±ã®æ°ã«éããçããããšãããããã§ãã
ãã£ãã転è·ãµã€ããå©çšããã®ã§ããã°ãã§ããã ãæ°å€ãã®æ±äººæ å ±ã®äžããèªåã®æ¡ä»¶ã«ãã£ãæ±äººæ å ±ãæ¢ããæ¹ãè¯ãã¯ãã§ãã
ãã®åè€æ°ã®ã³ãŒãã£ããŒã¿ãŒã®æ¹ãšè©±ãããå¿ èŠããããŸãããèªåã®ããããã®ãã£ãªã¢ã人çã圢äœã£ãŠããäžã§ã¯å¿ èŠãªããšã«ãªããŸãã
ãŸããã³ãŒãã£ããŒã¿ãŒã®æ¹ã人éã§ããããããããç¹æ§ããããŸãã
èªåã«åãåããªããšèšãããšããããŸãããããããã£ãæå³ã§ãè€æ°ãµã€ãã®ç»é²ã¯å€§åãããããŸããã
ãšã«ããè¡åïŒç»é²ïŒïŒç®¡ç人ãç»é²çµéšããïŒè»¢è·ãµã€ãã®ã玹ä»ïŒ
ãããæ€çŽ¢ã«ãã転è·ãµã€ãã®æ±äººæ å ±ã¯è¡šé¢äžã®æ å ±ã§ããææ°ã®ãã®ãããã°å€ãæ å ±ããããéå ¬éæ å ±ããããŸãã
åç é¢ãæœèšã¯ãå šãŠã®æ±äººæ å ±ãµã€ãã«ç»é²ããèš³ã§ã¯ãªãã®ã§ãè€æ°ç»é²ããäºã§ ããå€ãã®æ±äººæ å ±ã«è§Šããäºãã§ããŸãã
管ç人ã®çµéšäžã§ããããŸãã¯è峿¬äœã§ç»é²ããã®ãããããªãšæããŸãã
è¡ååãè¶³ããªãæ¹ãã話ãèããŠãããã¡ã«åãåæ°ãšè¡ååãæ¹§ããŠããããšããããŸãã
転è·çç±ã¯äººããããã§ãããæºè¶³ã§ãã転è·ã«ãªãããã«é¡ã£ãŠããŸãã
管ç人ã®è»¢è·çµéšã«ã€ããŠã¯ä»¥äžã®èšäºãåç §ããŠãã ããã
ãäœæ¥çæ³å£«ã«ãªãã«ã¯ãããªã£ãåŸã®ãã£ãªã¢åœ¢æãããåãããã絊äžã転è·ãä»äºã®æ¬é³ããŸããããèŸå ž
転è·ãµã€ãäžèŠ§ïŒæ±äººæ å ±ïŒéå ¬éæ å ±ãå«ãïŒãèŠãã«ã¯å転è·ãµã€ãã«ç§»åããç¡æç»é²ããå¿ èŠããããŸãïŒ
â PT/OT/STã®è»¢è·ç޹ä»ãªãããã€ããã³ã¡ãã£ã«ã«ã
â¡çåŠçæ³å£«/äœæ¥çæ³å£«å°éã®è»¢è·æ¯æŽãµãŒãã¹ãPTOTãã£ãªã¢ããã
