Identification of patients eligible for discharge within 48 h of colorectal resection

Abstract

Background This study aimed to identify patients eligible for a 48‐h stay after colorectal resection, to provide guidance for early discharge planning. Methods A bi‐institutional retrospective cohort study was undertaken of consecutive patients undergoing major elective colorectal resection for benign or malignant pathology within a comprehensive enhanced recovery pathway between 2011 and 2017. Overall and severe (Clavien–Dindo grade IIIb or above) postoperative complication and readmission rates were compared between patients who were discharged within 48 h and those who had hospital stay of 48 h or more. Multinominal logistic regression analysis was performed to ascertain significant factors associated with a short hospital stay (less than 48 h). Results In total, 686 of 5122 patients (13·4 per cent) were discharged within 48 h. Independent factors favouring a short hospital stay were age below 60 years (odds ratio (OR) 1·34; P = 0·002), ASA grade less than III (OR 1·42; P = 0·003), restrictive fluid management (less than 3000 ml on day of surgery: OR 1·46; P < 0·001), duration of surgery less than 180 min (OR 1·89; P < 0·001), minimally invasive approach (OR 1·92; P < 0·001) and wound contamination grade below III (OR 4·50; P < 0·001), whereas cancer diagnosis (OR 0·55; P < 0·001) and malnutrition (BMI below 18 kg/m2: OR 0·42; P = 0·008) decreased the likelihood of early discharge. Patients with a 48‐h stay had fewer overall (10·8 per cent versus 30·6 per cent in those with a longer stay; P < 0·001) and fewer severe (2·6 versus 10·2 per cent respectively; P < 0·001) complications, and a lower readmission rate (9·0 versus 11·8 per cent; P = 0·035). Conclusion Early discharge of selected patients is safe and does not increase postoperative morbidity or readmission rates. In these patients, outpatient colorectal surgery should be feasible on a large scale with logistical optimization.

Feasibility of discharge within 72 hours of major colorectal surgery: lessons learned after 5 years of institutional experience with the ERAS protocol

“…In another recent paper summarizing the experience of two high-volume colorectal centres in the USA and Switzerland, 13.4 per cent of patients were discharged within 48 h. This approach was associated with patient age of less than 60 years, a lower ASA grade, restrictive fluid management, a shorter duration of surgery, and a minimally invasive approach. The early-discharge group was associated with lower rates of postoperative complications, major complications, and reintervention 8 .…”

Verbreitung von Fast-Track-Konzepten in Deutschland

“…Die Evidenz, insbesondere im Bereich der kolorektalen Chirurgie, ist mittlerweile belastbar [ 1 – 4 , 6 ] und wird im kommenden Jahr in einer S3-Leitlinie der AWMF (Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften) münden ( Perioperatives Management bei gastrointestinalen Tumoren – POMGAT). Durch eine Ausrichtung der perioperativen Betreuung auf evidenzbasierte Maßnahmen sind Liegezeiten nach kolorektalen Resektion zwischen 24 und 48 h möglich [ 7 , 8 ]. Dies wird durch eine Reduktion der Komplikationsrate, die im Rahmen von Fast-Track-Konzepten beobachtet wurde, bei gleichbleibender Wiederaufnahme- und Mortalitätsrate im Vergleich zum konventionellen perioperativen Management, in welchem Fast-Track-Prinzipien nicht vollständig umgesetzt sind, erreicht [ 9 , 10 ].…”

“…Die mediane Liegedauer bei onkologischen kolorektalen Resektionen betrug 2021 in der hier vorgestellten Patientenkohorte 11 Tage, bei einer Sigmaresektion aufgrund einer Divertikulitis 9 Tage. Von den zuvor erwähnten 24–48 h, die zumindest in Studien [ 7 , 8 ] realisierbar waren, sind diese Zahlen weit entfernt. Das deutsche Gesundheitssystem ist durch eine im internationalen Vergleich hohe Anzahl an Krankenhausbetten gekennzeichnet.…”

Preoperative risk factors for major postoperative complications after complex gastrointestinal cancer surgery: A systematic review

“…With increasingly larger datasets, machine learning holds the potential to unravel subtle associations that are notdor cannotdbe identified using classic regression approaches. For suspected low-risk patients, machine learning has been suggested to support early discharge decisions [77]. Suspected high-risk patients may benefit from closer postoperative surveillance.…”